best_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:50.002947847Z",
     "start_time": "2023-10-05T18:31:48.428658314Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in ./venv/lib/python3.11/site-packages (2.0.0)\r\n",
      "Requirement already satisfied: numpy in ./venv/lib64/python3.11/site-packages (from xgboost) (1.26.0)\r\n",
      "Requirement already satisfied: scipy in ./venv/lib64/python3.11/site-packages (from xgboost) (1.11.3)\r\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:50.006629231Z",
     "start_time": "2023-10-05T18:31:50.004718769Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:50.083666979Z",
     "start_time": "2023-10-05T18:31:50.008238901Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                        Species  ForStrat_terrestrial  ForStrat_aquatic  \\\n0                Neovison vison                   1.0               0.0   \n1              Acinonyx jubatus                   1.0               0.0   \n2               Aotus nancymaae                   1.0               0.0   \n3    Balaenoptera acutorostrata                   0.0               1.0   \n4                     Bos mutus                   NaN               NaN   \n..                          ...                   ...               ...   \n108                Ursus arctos                   1.0               0.0   \n109             Ursus maritimus                   1.0               0.0   \n110            Vombatus ursinus                   1.0               0.0   \n111               Vulpes vulpes                   1.0               0.0   \n112      Zalophus californianus                   0.0               1.0   \n\n     Activity.Nocturnal  Activity.Crepuscular  Activity.Diurnal  \\\n0                   1.0                   1.0               0.0   \n1                   0.0                   0.0               1.0   \n2                   1.0                   0.0               0.0   \n3                   0.0                   0.0               1.0   \n4                   NaN                   NaN               NaN   \n..                  ...                   ...               ...   \n108                 0.0                   1.0               1.0   \n109                 0.0                   1.0               1.0   \n110                 1.0                   0.0               0.0   \n111                 1.0                   1.0               0.0   \n112                 1.0                   1.0               0.0   \n\n     female_maturity_d  male_maturity_d   weaning_d  \\\n0           297.722620       371.000000   49.000000   \n1           598.985000       456.000000  107.000000   \n2           683.548198       627.229743  199.718698   \n3          2740.000000      2740.000000  167.340000   \n4           494.972661       702.981099   90.199617   \n..                 ...              ...         ...   \n108        1320.475000      1313.000000  213.000000   \n109        1734.000000      1734.000000  326.440000   \n110         742.575000       730.000000  398.725000   \n111         304.000000       304.000000   49.710000   \n112        1643.625000      1826.000000  315.000000   \n\n     litters_or_clutches_per_y  longevity_y  adult_svl_cm  \\\n0                     1.000000    10.695833     43.000000   \n1                     0.700000    19.000000    149.021500   \n2                     1.373433    11.391667     37.000000   \n3                     1.000000    48.500000    786.465500   \n4                     1.068823    11.245579     38.234507   \n..                         ...          ...           ...   \n108                   0.400000    43.500000    228.500000   \n109                   0.400000    43.775000    224.627500   \n110                   0.770000    26.100000    109.339000   \n111                   1.000000    15.000000     63.000000   \n112                   0.890000    30.000000    225.948500   \n\n     infantMortalityRate_per_year  mortalityRateDoublingTime_y  \\\n0                             NaN                          NaN   \n1                             NaN                          NaN   \n2                             NaN                          NaN   \n3                             NaN                          NaN   \n4                             NaN                          NaN   \n..                            ...                          ...   \n108                           NaN                          NaN   \n109                           NaN                          NaN   \n110                           NaN                          NaN   \n111                           NaN                          NaN   \n112                           NaN                          NaN   \n\n     metabolicRate_W  temperature_K  diet_breadth  development_d  \\\n0              2.722         312.15           0.4      36.220620   \n1             61.770         312.15           0.1      89.060000   \n2                NaN            NaN           0.6     127.745221   \n3                NaN            NaN           0.2     309.306350   \n4                NaN            NaN           NaN     120.038185   \n..               ...            ...           ...            ...   \n108              NaN         310.45           0.4     103.000000   \n109              NaN         309.95           0.2     106.207970   \n110              NaN            NaN           0.1      27.750000   \n111           13.731         311.85           0.3      52.176250   \n112              NaN         311.25           0.2     296.920000   \n\n     mass_specific_production  log_litterclutch_size_n  \\\n0                    0.045709                 1.560248   \n1                    0.019578                 1.111858   \n2                    0.153402                 0.019803   \n3                    0.042667                 0.000000   \n4                    0.407257                 0.526610   \n..                        ...                      ...   \n108                  0.001888                 0.819780   \n109                  0.001160                 0.506818   \n110                  0.000015                 0.000000   \n111                  0.103829                 1.554454   \n112                  0.041720                 0.095310   \n\n     log_inter_litterbirth_interval_y  log_birthhatching_weight_g  \\\n0                           -0.000685                    2.156403   \n1                            0.395145                    6.142747   \n2                           -0.116040                    4.275836   \n3                            0.252002                   12.676076   \n4                           -0.172863                    2.924968   \n..                                ...                         ...   \n108                          0.881893                    6.214588   \n109                          0.772507                    6.476395   \n110                          0.692462                   -0.703198   \n111                         -0.000685                    4.610058   \n112                          0.692462                    8.818567   \n\n     log_weaning_weight_g  log_adult_body_mass_g  log_female_body_mass_g  \\\n0                4.700480               6.802117                7.013985   \n1                7.570443              10.831270                9.846167   \n2                6.129428               6.673336                7.444762   \n3               14.492895              15.830414               10.918308   \n4                5.166991                    NaN                5.248068   \n..                    ...                    ...                     ...   \n108             10.165852              12.390475               10.785992   \n109              9.784141              12.825853               11.118941   \n110              8.648221              10.156190                9.974803   \n111              7.242082               8.429520                8.992424   \n112             10.126631              11.974124               11.608236   \n\n     log_male_body_mass_g  tnc_ecoregion_breadth  log_range_size  \\\n0                7.444249              27.000000       16.494735   \n1               10.988802               0.309524       15.448508   \n2                6.720834               0.083333       13.107712   \n3               14.592295               0.559524       18.728611   \n4                6.018212               0.047619       13.059399   \n..                    ...                    ...             ...   \n108             12.105700               0.500000       17.405000   \n109             12.964539               0.166667       16.933700   \n110             11.353427               0.095238       12.645709   \n111              8.741935               0.619048       18.097751   \n112             12.873902               0.047619       14.314292   \n\n     X2.1_AgeatEyeOpening_d  X9.1_GestationLen_d  X10.2_SocialGrpSize  \\\n0                       NaN            36.705987                  NaN   \n1                      9.03            92.240000                  NaN   \n2                       NaN           108.672743                  4.0   \n3                       NaN           304.160000                  2.0   \n4                       NaN           108.754439                  NaN   \n..                      ...                  ...                  ...   \n108                   31.82           227.560000                  NaN   \n109                   34.12            64.660000                  NaN   \n110                     NaN            30.000000                  NaN   \n111                   14.01            52.500000                  NaN   \n112                    0.00           349.990000                  NaN   \n\n     X24.1_TeatNumber  X6.2_TrophicLevel  X13.3_WeaningHeadBodyLen_mm  \\\n0                 NaN                NaN                          NaN   \n1                12.0                3.0                          NaN   \n2                 NaN                2.0                          NaN   \n3                 NaN                3.0                          NaN   \n4                 NaN                NaN                          NaN   \n..                ...                ...                          ...   \n108               NaN                2.0                          NaN   \n109               4.0                2.0                          NaN   \n110               NaN                1.0                          NaN   \n111               8.0                3.0                        300.0   \n112               NaN                2.0                          NaN   \n\n     X26.2_GR_MaxLat_dd  X26.3_GR_MinLat_dd  X26.4_GR_MidRangeLat_dd  \\\n0             71.390000           24.940000                48.160000   \n1             37.720000          -29.220000                 4.240000   \n2             -2.720000           -6.470000                -4.590000   \n3             11.659394            0.560051                 6.201413   \n4             13.695438           -0.794176                 6.201413   \n..                  ...                 ...                      ...   \n108           72.380000           29.140000                50.760000   \n109           10.828224           -0.169243                 6.201413   \n110          -28.470000          -43.640000               -36.050000   \n111           74.040000           12.160000                43.100000   \n112           13.860632           -0.732860                 6.201413   \n\n     X26.5_GR_MaxLong_dd  X26.6_GR_MinLong_dd  X26.7_GR_MidRangeLong_dd  \\\n0             -55.610000          -168.120000               -111.870000   \n1              60.820000           -14.910000                 22.950000   \n2             -66.610000           -75.950000                -71.280000   \n3              35.846219            14.805832                 25.230358   \n4              35.846219            14.805832                 25.230358   \n..                   ...                  ...                       ...   \n108           179.990000          -168.120000                  5.930000   \n109            35.846219            14.805832                 25.230358   \n110           152.320000           138.710000                145.510000   \n111           179.990000          -179.990000                  0.000000   \n112            35.846219            14.805832                 25.230358   \n\n     X27.4_HuPopDen_Change  X28.1_Precip_Mean_mm  X28.2_Temp_Mean_01degC  \\\n0                 0.030000             55.370000               14.910000   \n1                 0.110000             51.260000              230.830000   \n2                 0.050000            228.040000              261.240000   \n3                      NaN            148.434830              177.854437   \n4                      NaN            149.293170              181.553030   \n..                     ...                   ...                     ...   \n108               0.004331             35.010000              -46.560000   \n109                    NaN            149.443347              182.136850   \n110               0.010000             64.510000              129.650000   \n111               0.040000             46.580000               34.320000   \n112                    NaN            149.336948              181.948970   \n\n     X30.1_AET_Mean_mm  X30.2_PET_Mean_mm  log_DispersalAge_d  \\\n0           458.260000         529.250000                 NaN   \n1           595.060000        1499.040000            6.142037   \n2          1531.250000        1533.130000                 NaN   \n3           894.442357        1307.267867                 NaN   \n4           875.991850        1326.443988                 NaN   \n..                 ...                ...                 ...   \n108         292.870000         408.250000            6.816177   \n109         866.298574        1307.923217            6.710511   \n110         610.730000         973.380000                 NaN   \n111         407.980000         646.290000            5.416100   \n112         851.319282        1328.334542                 NaN   \n\n     log_HomeRange_km2  log_HomeRange_Indiv_km2  log_PopulationDensity_n.km2  \\\n0            -2.866854                -2.754329                     4.085635   \n1             4.480400                 4.547329                    -4.605170   \n2            -2.939557                -2.663378                     3.337903   \n3             2.442211                -2.458595                    -1.034633   \n4            -1.319838                -2.781916                     5.082738   \n..                 ...                      ...                          ...   \n108           5.739182                 5.810602                    -3.912023   \n109          11.255067                11.326481                    -4.954954   \n110          -2.525729                -2.659260                     3.255015   \n111           1.252763                 1.255616                     0.095310   \n112           2.291362                -2.322158                    -0.693147   \n\n     log_PopulationGrpSize  log_HuPopDen_Min_n.km2  log_HuPopDen_Mean_n.km2  \\\n0                      NaN                1.056682                 3.006178   \n1                      NaN                0.907587                 2.989714   \n2                      NaN                1.669032                 0.122218   \n3                      0.0                1.632281                 1.625241   \n4                      NaN                1.847101                 1.977512   \n..                     ...                     ...                      ...   \n108                    NaN                0.923684                 2.698000   \n109                    0.0                1.641823                 1.700675   \n110                    0.0                0.189470                 3.266141   \n111                    NaN                0.481049                 4.196299   \n112                    NaN                1.600494                 1.707005   \n\n     log_HuPopDen_5p_n.km2  log_NeonateHeadBodyLen_mm  log_WOS_hits  \\\n0                 0.025741                   3.803203      7.557473   \n1                -0.067474                   5.521461      6.694562   \n2                 0.455452                   4.046583      4.700481   \n3                 0.654467                   8.935902      6.159096   \n4                 0.721810                   3.975580      3.663564   \n..                     ...                        ...           ...   \n108              -0.059683                   6.110936      7.664347   \n109               0.589617                   6.116348      7.138867   \n110               0.000000                   3.090946      5.111988   \n111              -0.072112                   5.356510      8.300529   \n112               0.746955                   6.534284      6.870054   \n\n     Afrosoricida  Artiodactyla  Carnivora  Cetacea  Chiroptera  Cingulata  \\\n0               0             0          1        0           0          0   \n1               0             0          1        0           0          0   \n2               0             0          0        0           0          0   \n3               0             0          0        1           0          0   \n4               0             1          0        0           0          0   \n..            ...           ...        ...      ...         ...        ...   \n108             0             0          1        0           0          0   \n109             0             0          1        0           0          0   \n110             0             0          0        0           0          0   \n111             0             0          1        0           0          0   \n112             0             0          1        0           0          0   \n\n     Dasyuromorphia  Didelphimorphia  Diprotodontia  Erinaceomorpha  \\\n0                 0                0              0               0   \n1                 0                0              0               0   \n2                 0                0              0               0   \n3                 0                0              0               0   \n4                 0                0              0               0   \n..              ...              ...            ...             ...   \n108               0                0              0               0   \n109               0                0              0               0   \n110               0                0              1               0   \n111               0                0              0               0   \n112               0                0              0               0   \n\n     Lagomorpha  Macroscelidea  Monotremata  Perissodactyla  Pholidota  \\\n0             0              0            0               0          0   \n1             0              0            0               0          0   \n2             0              0            0               0          0   \n3             0              0            0               0          0   \n4             0              0            0               0          0   \n..          ...            ...          ...             ...        ...   \n108           0              0            0               0          0   \n109           0              0            0               0          0   \n110           0              0            0               0          0   \n111           0              0            0               0          0   \n112           0              0            0               0          0   \n\n     Primates  Proboscidea  Rodentia  Sirenia  Soricomorpha  Tubulidentata  \\\n0           0            0         0        0             0              0   \n1           0            0         0        0             0              0   \n2           1            0         0        0             0              0   \n3           0            0         0        0             0              0   \n4           0            0         0        0             0              0   \n..        ...          ...       ...      ...           ...            ...   \n108         0            0         0        0             0              0   \n109         0            0         0        0             0              0   \n110         0            0         0        0             0              0   \n111         0            0         0        0             0              0   \n112         0            0         0        0             0              0   \n\n     nchar  haddock_score_mean  haddock_score_sd  AA_83_Y  AA_30_negative  \n0      805         -144.814760          1.580668        1               1  \n1      805         -127.078110          1.747960        1               1  \n2      805         -140.619180          2.629311        1               1  \n3      804         -117.739420          2.003812        1               0  \n4      804         -128.363990          2.843593        1               1  \n..     ...                 ...               ...      ...             ...  \n108    805         -164.694640          3.378159        1               1  \n109    790         -148.124260          2.376873        1               1  \n110    809         -125.032739          3.420254        0               1  \n111    804         -141.373520          4.313306        1               1  \n112    806         -129.135090          3.579590        1               1  \n\n[113 rows x 81 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Species</th>\n      <th>ForStrat_terrestrial</th>\n      <th>ForStrat_aquatic</th>\n      <th>Activity.Nocturnal</th>\n      <th>Activity.Crepuscular</th>\n      <th>Activity.Diurnal</th>\n      <th>female_maturity_d</th>\n      <th>male_maturity_d</th>\n      <th>weaning_d</th>\n      <th>litters_or_clutches_per_y</th>\n      <th>longevity_y</th>\n      <th>adult_svl_cm</th>\n      <th>infantMortalityRate_per_year</th>\n      <th>mortalityRateDoublingTime_y</th>\n      <th>metabolicRate_W</th>\n      <th>temperature_K</th>\n      <th>diet_breadth</th>\n      <th>development_d</th>\n      <th>mass_specific_production</th>\n      <th>log_litterclutch_size_n</th>\n      <th>log_inter_litterbirth_interval_y</th>\n      <th>log_birthhatching_weight_g</th>\n      <th>log_weaning_weight_g</th>\n      <th>log_adult_body_mass_g</th>\n      <th>log_female_body_mass_g</th>\n      <th>log_male_body_mass_g</th>\n      <th>tnc_ecoregion_breadth</th>\n      <th>log_range_size</th>\n      <th>X2.1_AgeatEyeOpening_d</th>\n      <th>X9.1_GestationLen_d</th>\n      <th>X10.2_SocialGrpSize</th>\n      <th>X24.1_TeatNumber</th>\n      <th>X6.2_TrophicLevel</th>\n      <th>X13.3_WeaningHeadBodyLen_mm</th>\n      <th>X26.2_GR_MaxLat_dd</th>\n      <th>X26.3_GR_MinLat_dd</th>\n      <th>X26.4_GR_MidRangeLat_dd</th>\n      <th>X26.5_GR_MaxLong_dd</th>\n      <th>X26.6_GR_MinLong_dd</th>\n      <th>X26.7_GR_MidRangeLong_dd</th>\n      <th>X27.4_HuPopDen_Change</th>\n      <th>X28.1_Precip_Mean_mm</th>\n      <th>X28.2_Temp_Mean_01degC</th>\n      <th>X30.1_AET_Mean_mm</th>\n      <th>X30.2_PET_Mean_mm</th>\n      <th>log_DispersalAge_d</th>\n      <th>log_HomeRange_km2</th>\n      <th>log_HomeRange_Indiv_km2</th>\n      <th>log_PopulationDensity_n.km2</th>\n      <th>log_PopulationGrpSize</th>\n      <th>log_HuPopDen_Min_n.km2</th>\n      <th>log_HuPopDen_Mean_n.km2</th>\n      <th>log_HuPopDen_5p_n.km2</th>\n      <th>log_NeonateHeadBodyLen_mm</th>\n      <th>log_WOS_hits</th>\n      <th>Afrosoricida</th>\n      <th>Artiodactyla</th>\n      <th>Carnivora</th>\n      <th>Cetacea</th>\n      <th>Chiroptera</th>\n      <th>Cingulata</th>\n      <th>Dasyuromorphia</th>\n      <th>Didelphimorphia</th>\n      <th>Diprotodontia</th>\n      <th>Erinaceomorpha</th>\n      <th>Lagomorpha</th>\n      <th>Macroscelidea</th>\n      <th>Monotremata</th>\n      <th>Perissodactyla</th>\n      <th>Pholidota</th>\n      <th>Primates</th>\n      <th>Proboscidea</th>\n      <th>Rodentia</th>\n      <th>Sirenia</th>\n      <th>Soricomorpha</th>\n      <th>Tubulidentata</th>\n      <th>nchar</th>\n      <th>haddock_score_mean</th>\n      <th>haddock_score_sd</th>\n      <th>AA_83_Y</th>\n      <th>AA_30_negative</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Neovison vison</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>297.722620</td>\n      <td>371.000000</td>\n      <td>49.000000</td>\n      <td>1.000000</td>\n      <td>10.695833</td>\n      <td>43.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.722</td>\n      <td>312.15</td>\n      <td>0.4</td>\n      <td>36.220620</td>\n      <td>0.045709</td>\n      <td>1.560248</td>\n      <td>-0.000685</td>\n      <td>2.156403</td>\n      <td>4.700480</td>\n      <td>6.802117</td>\n      <td>7.013985</td>\n      <td>7.444249</td>\n      <td>27.000000</td>\n      <td>16.494735</td>\n      <td>NaN</td>\n      <td>36.705987</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>71.390000</td>\n      <td>24.940000</td>\n      <td>48.160000</td>\n      <td>-55.610000</td>\n      <td>-168.120000</td>\n      <td>-111.870000</td>\n      <td>0.030000</td>\n      <td>55.370000</td>\n      <td>14.910000</td>\n      <td>458.260000</td>\n      <td>529.250000</td>\n      <td>NaN</td>\n      <td>-2.866854</td>\n      <td>-2.754329</td>\n      <td>4.085635</td>\n      <td>NaN</td>\n      <td>1.056682</td>\n      <td>3.006178</td>\n      <td>0.025741</td>\n      <td>3.803203</td>\n      <td>7.557473</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>805</td>\n      <td>-144.814760</td>\n      <td>1.580668</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Acinonyx jubatus</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>598.985000</td>\n      <td>456.000000</td>\n      <td>107.000000</td>\n      <td>0.700000</td>\n      <td>19.000000</td>\n      <td>149.021500</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>61.770</td>\n      <td>312.15</td>\n      <td>0.1</td>\n      <td>89.060000</td>\n      <td>0.019578</td>\n      <td>1.111858</td>\n      <td>0.395145</td>\n      <td>6.142747</td>\n      <td>7.570443</td>\n      <td>10.831270</td>\n      <td>9.846167</td>\n      <td>10.988802</td>\n      <td>0.309524</td>\n      <td>15.448508</td>\n      <td>9.03</td>\n      <td>92.240000</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>37.720000</td>\n      <td>-29.220000</td>\n      <td>4.240000</td>\n      <td>60.820000</td>\n      <td>-14.910000</td>\n      <td>22.950000</td>\n      <td>0.110000</td>\n      <td>51.260000</td>\n      <td>230.830000</td>\n      <td>595.060000</td>\n      <td>1499.040000</td>\n      <td>6.142037</td>\n      <td>4.480400</td>\n      <td>4.547329</td>\n      <td>-4.605170</td>\n      <td>NaN</td>\n      <td>0.907587</td>\n      <td>2.989714</td>\n      <td>-0.067474</td>\n      <td>5.521461</td>\n      <td>6.694562</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>805</td>\n      <td>-127.078110</td>\n      <td>1.747960</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Aotus nancymaae</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>683.548198</td>\n      <td>627.229743</td>\n      <td>199.718698</td>\n      <td>1.373433</td>\n      <td>11.391667</td>\n      <td>37.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.6</td>\n      <td>127.745221</td>\n      <td>0.153402</td>\n      <td>0.019803</td>\n      <td>-0.116040</td>\n      <td>4.275836</td>\n      <td>6.129428</td>\n      <td>6.673336</td>\n      <td>7.444762</td>\n      <td>6.720834</td>\n      <td>0.083333</td>\n      <td>13.107712</td>\n      <td>NaN</td>\n      <td>108.672743</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>-2.720000</td>\n      <td>-6.470000</td>\n      <td>-4.590000</td>\n      <td>-66.610000</td>\n      <td>-75.950000</td>\n      <td>-71.280000</td>\n      <td>0.050000</td>\n      <td>228.040000</td>\n      <td>261.240000</td>\n      <td>1531.250000</td>\n      <td>1533.130000</td>\n      <td>NaN</td>\n      <td>-2.939557</td>\n      <td>-2.663378</td>\n      <td>3.337903</td>\n      <td>NaN</td>\n      <td>1.669032</td>\n      <td>0.122218</td>\n      <td>0.455452</td>\n      <td>4.046583</td>\n      <td>4.700481</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>805</td>\n      <td>-140.619180</td>\n      <td>2.629311</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Balaenoptera acutorostrata</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2740.000000</td>\n      <td>2740.000000</td>\n      <td>167.340000</td>\n      <td>1.000000</td>\n      <td>48.500000</td>\n      <td>786.465500</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.2</td>\n      <td>309.306350</td>\n      <td>0.042667</td>\n      <td>0.000000</td>\n      <td>0.252002</td>\n      <td>12.676076</td>\n      <td>14.492895</td>\n      <td>15.830414</td>\n      <td>10.918308</td>\n      <td>14.592295</td>\n      <td>0.559524</td>\n      <td>18.728611</td>\n      <td>NaN</td>\n      <td>304.160000</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>11.659394</td>\n      <td>0.560051</td>\n      <td>6.201413</td>\n      <td>35.846219</td>\n      <td>14.805832</td>\n      <td>25.230358</td>\n      <td>NaN</td>\n      <td>148.434830</td>\n      <td>177.854437</td>\n      <td>894.442357</td>\n      <td>1307.267867</td>\n      <td>NaN</td>\n      <td>2.442211</td>\n      <td>-2.458595</td>\n      <td>-1.034633</td>\n      <td>0.0</td>\n      <td>1.632281</td>\n      <td>1.625241</td>\n      <td>0.654467</td>\n      <td>8.935902</td>\n      <td>6.159096</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>804</td>\n      <td>-117.739420</td>\n      <td>2.003812</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bos mutus</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>494.972661</td>\n      <td>702.981099</td>\n      <td>90.199617</td>\n      <td>1.068823</td>\n      <td>11.245579</td>\n      <td>38.234507</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>120.038185</td>\n      <td>0.407257</td>\n      <td>0.526610</td>\n      <td>-0.172863</td>\n      <td>2.924968</td>\n      <td>5.166991</td>\n      <td>NaN</td>\n      <td>5.248068</td>\n      <td>6.018212</td>\n      <td>0.047619</td>\n      <td>13.059399</td>\n      <td>NaN</td>\n      <td>108.754439</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>13.695438</td>\n      <td>-0.794176</td>\n      <td>6.201413</td>\n      <td>35.846219</td>\n      <td>14.805832</td>\n      <td>25.230358</td>\n      <td>NaN</td>\n      <td>149.293170</td>\n      <td>181.553030</td>\n      <td>875.991850</td>\n      <td>1326.443988</td>\n      <td>NaN</td>\n      <td>-1.319838</td>\n      <td>-2.781916</td>\n      <td>5.082738</td>\n      <td>NaN</td>\n      <td>1.847101</td>\n      <td>1.977512</td>\n      <td>0.721810</td>\n      <td>3.975580</td>\n      <td>3.663564</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>804</td>\n      <td>-128.363990</td>\n      <td>2.843593</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>Ursus arctos</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1320.475000</td>\n      <td>1313.000000</td>\n      <td>213.000000</td>\n      <td>0.400000</td>\n      <td>43.500000</td>\n      <td>228.500000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>310.45</td>\n      <td>0.4</td>\n      <td>103.000000</td>\n      <td>0.001888</td>\n      <td>0.819780</td>\n      <td>0.881893</td>\n      <td>6.214588</td>\n      <td>10.165852</td>\n      <td>12.390475</td>\n      <td>10.785992</td>\n      <td>12.105700</td>\n      <td>0.500000</td>\n      <td>17.405000</td>\n      <td>31.82</td>\n      <td>227.560000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>72.380000</td>\n      <td>29.140000</td>\n      <td>50.760000</td>\n      <td>179.990000</td>\n      <td>-168.120000</td>\n      <td>5.930000</td>\n      <td>0.004331</td>\n      <td>35.010000</td>\n      <td>-46.560000</td>\n      <td>292.870000</td>\n      <td>408.250000</td>\n      <td>6.816177</td>\n      <td>5.739182</td>\n      <td>5.810602</td>\n      <td>-3.912023</td>\n      <td>NaN</td>\n      <td>0.923684</td>\n      <td>2.698000</td>\n      <td>-0.059683</td>\n      <td>6.110936</td>\n      <td>7.664347</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>805</td>\n      <td>-164.694640</td>\n      <td>3.378159</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>Ursus maritimus</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1734.000000</td>\n      <td>1734.000000</td>\n      <td>326.440000</td>\n      <td>0.400000</td>\n      <td>43.775000</td>\n      <td>224.627500</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>309.95</td>\n      <td>0.2</td>\n      <td>106.207970</td>\n      <td>0.001160</td>\n      <td>0.506818</td>\n      <td>0.772507</td>\n      <td>6.476395</td>\n      <td>9.784141</td>\n      <td>12.825853</td>\n      <td>11.118941</td>\n      <td>12.964539</td>\n      <td>0.166667</td>\n      <td>16.933700</td>\n      <td>34.12</td>\n      <td>64.660000</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>10.828224</td>\n      <td>-0.169243</td>\n      <td>6.201413</td>\n      <td>35.846219</td>\n      <td>14.805832</td>\n      <td>25.230358</td>\n      <td>NaN</td>\n      <td>149.443347</td>\n      <td>182.136850</td>\n      <td>866.298574</td>\n      <td>1307.923217</td>\n      <td>6.710511</td>\n      <td>11.255067</td>\n      <td>11.326481</td>\n      <td>-4.954954</td>\n      <td>0.0</td>\n      <td>1.641823</td>\n      <td>1.700675</td>\n      <td>0.589617</td>\n      <td>6.116348</td>\n      <td>7.138867</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>790</td>\n      <td>-148.124260</td>\n      <td>2.376873</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>Vombatus ursinus</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>742.575000</td>\n      <td>730.000000</td>\n      <td>398.725000</td>\n      <td>0.770000</td>\n      <td>26.100000</td>\n      <td>109.339000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.1</td>\n      <td>27.750000</td>\n      <td>0.000015</td>\n      <td>0.000000</td>\n      <td>0.692462</td>\n      <td>-0.703198</td>\n      <td>8.648221</td>\n      <td>10.156190</td>\n      <td>9.974803</td>\n      <td>11.353427</td>\n      <td>0.095238</td>\n      <td>12.645709</td>\n      <td>NaN</td>\n      <td>30.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>-28.470000</td>\n      <td>-43.640000</td>\n      <td>-36.050000</td>\n      <td>152.320000</td>\n      <td>138.710000</td>\n      <td>145.510000</td>\n      <td>0.010000</td>\n      <td>64.510000</td>\n      <td>129.650000</td>\n      <td>610.730000</td>\n      <td>973.380000</td>\n      <td>NaN</td>\n      <td>-2.525729</td>\n      <td>-2.659260</td>\n      <td>3.255015</td>\n      <td>0.0</td>\n      <td>0.189470</td>\n      <td>3.266141</td>\n      <td>0.000000</td>\n      <td>3.090946</td>\n      <td>5.111988</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>809</td>\n      <td>-125.032739</td>\n      <td>3.420254</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>Vulpes vulpes</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>304.000000</td>\n      <td>304.000000</td>\n      <td>49.710000</td>\n      <td>1.000000</td>\n      <td>15.000000</td>\n      <td>63.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>13.731</td>\n      <td>311.85</td>\n      <td>0.3</td>\n      <td>52.176250</td>\n      <td>0.103829</td>\n      <td>1.554454</td>\n      <td>-0.000685</td>\n      <td>4.610058</td>\n      <td>7.242082</td>\n      <td>8.429520</td>\n      <td>8.992424</td>\n      <td>8.741935</td>\n      <td>0.619048</td>\n      <td>18.097751</td>\n      <td>14.01</td>\n      <td>52.500000</td>\n      <td>NaN</td>\n      <td>8.0</td>\n      <td>3.0</td>\n      <td>300.0</td>\n      <td>74.040000</td>\n      <td>12.160000</td>\n      <td>43.100000</td>\n      <td>179.990000</td>\n      <td>-179.990000</td>\n      <td>0.000000</td>\n      <td>0.040000</td>\n      <td>46.580000</td>\n      <td>34.320000</td>\n      <td>407.980000</td>\n      <td>646.290000</td>\n      <td>5.416100</td>\n      <td>1.252763</td>\n      <td>1.255616</td>\n      <td>0.095310</td>\n      <td>NaN</td>\n      <td>0.481049</td>\n      <td>4.196299</td>\n      <td>-0.072112</td>\n      <td>5.356510</td>\n      <td>8.300529</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>804</td>\n      <td>-141.373520</td>\n      <td>4.313306</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>Zalophus californianus</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1643.625000</td>\n      <td>1826.000000</td>\n      <td>315.000000</td>\n      <td>0.890000</td>\n      <td>30.000000</td>\n      <td>225.948500</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>311.25</td>\n      <td>0.2</td>\n      <td>296.920000</td>\n      <td>0.041720</td>\n      <td>0.095310</td>\n      <td>0.692462</td>\n      <td>8.818567</td>\n      <td>10.126631</td>\n      <td>11.974124</td>\n      <td>11.608236</td>\n      <td>12.873902</td>\n      <td>0.047619</td>\n      <td>14.314292</td>\n      <td>0.00</td>\n      <td>349.990000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>13.860632</td>\n      <td>-0.732860</td>\n      <td>6.201413</td>\n      <td>35.846219</td>\n      <td>14.805832</td>\n      <td>25.230358</td>\n      <td>NaN</td>\n      <td>149.336948</td>\n      <td>181.948970</td>\n      <td>851.319282</td>\n      <td>1328.334542</td>\n      <td>NaN</td>\n      <td>2.291362</td>\n      <td>-2.322158</td>\n      <td>-0.693147</td>\n      <td>NaN</td>\n      <td>1.600494</td>\n      <td>1.707005</td>\n      <td>0.746955</td>\n      <td>6.534284</td>\n      <td>6.870054</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>806</td>\n      <td>-129.135090</td>\n      <td>3.579590</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>113 rows × 81 columns</p>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data\n",
    "data = pd.read_csv('./input/MammalTrainingData080221_NvAdded.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:50.152939133Z",
     "start_time": "2023-10-05T18:31:50.108877261Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                        Species  ForStrat_terrestrial  ForStrat_aquatic  \\\n0                Neovison vison                   1.0               0.0   \n1              Acinonyx jubatus                   1.0               0.0   \n2               Aotus nancymaae                   1.0               0.0   \n3    Balaenoptera acutorostrata                   0.0               1.0   \n4                     Bos mutus                   NaN               NaN   \n..                          ...                   ...               ...   \n108                Ursus arctos                   1.0               0.0   \n109             Ursus maritimus                   1.0               0.0   \n110            Vombatus ursinus                   1.0               0.0   \n111               Vulpes vulpes                   1.0               0.0   \n112      Zalophus californianus                   0.0               1.0   \n\n     Activity.Nocturnal  Activity.Crepuscular  Activity.Diurnal  \\\n0                   1.0                   1.0               0.0   \n1                   0.0                   0.0               1.0   \n2                   1.0                   0.0               0.0   \n3                   0.0                   0.0               1.0   \n4                   NaN                   NaN               NaN   \n..                  ...                   ...               ...   \n108                 0.0                   1.0               1.0   \n109                 0.0                   1.0               1.0   \n110                 1.0                   0.0               0.0   \n111                 1.0                   1.0               0.0   \n112                 1.0                   1.0               0.0   \n\n     female_maturity_d  male_maturity_d   weaning_d  \\\n0           297.722620       371.000000   49.000000   \n1           598.985000       456.000000  107.000000   \n2           683.548198       627.229743  199.718698   \n3          2740.000000      2740.000000  167.340000   \n4           494.972661       702.981099   90.199617   \n..                 ...              ...         ...   \n108        1320.475000      1313.000000  213.000000   \n109        1734.000000      1734.000000  326.440000   \n110         742.575000       730.000000  398.725000   \n111         304.000000       304.000000   49.710000   \n112        1643.625000      1826.000000  315.000000   \n\n     litters_or_clutches_per_y  longevity_y  adult_svl_cm  \\\n0                     1.000000    10.695833     43.000000   \n1                     0.700000    19.000000    149.021500   \n2                     1.373433    11.391667     37.000000   \n3                     1.000000    48.500000    786.465500   \n4                     1.068823    11.245579     38.234507   \n..                         ...          ...           ...   \n108                   0.400000    43.500000    228.500000   \n109                   0.400000    43.775000    224.627500   \n110                   0.770000    26.100000    109.339000   \n111                   1.000000    15.000000     63.000000   \n112                   0.890000    30.000000    225.948500   \n\n     infantMortalityRate_per_year  mortalityRateDoublingTime_y  \\\n0                             NaN                          NaN   \n1                             NaN                          NaN   \n2                             NaN                          NaN   \n3                             NaN                          NaN   \n4                             NaN                          NaN   \n..                            ...                          ...   \n108                           NaN                          NaN   \n109                           NaN                          NaN   \n110                           NaN                          NaN   \n111                           NaN                          NaN   \n112                           NaN                          NaN   \n\n     metabolicRate_W  temperature_K  diet_breadth  development_d  \\\n0              2.722         312.15           0.4      36.220620   \n1             61.770         312.15           0.1      89.060000   \n2                NaN            NaN           0.6     127.745221   \n3                NaN            NaN           0.2     309.306350   \n4                NaN            NaN           NaN     120.038185   \n..               ...            ...           ...            ...   \n108              NaN         310.45           0.4     103.000000   \n109              NaN         309.95           0.2     106.207970   \n110              NaN            NaN           0.1      27.750000   \n111           13.731         311.85           0.3      52.176250   \n112              NaN         311.25           0.2     296.920000   \n\n     mass_specific_production  log_litterclutch_size_n  \\\n0                    0.045709                 1.560248   \n1                    0.019578                 1.111858   \n2                    0.153402                 0.019803   \n3                    0.042667                 0.000000   \n4                    0.407257                 0.526610   \n..                        ...                      ...   \n108                  0.001888                 0.819780   \n109                  0.001160                 0.506818   \n110                  0.000015                 0.000000   \n111                  0.103829                 1.554454   \n112                  0.041720                 0.095310   \n\n     log_inter_litterbirth_interval_y  log_birthhatching_weight_g  \\\n0                           -0.000685                    2.156403   \n1                            0.395145                    6.142747   \n2                           -0.116040                    4.275836   \n3                            0.252002                   12.676076   \n4                           -0.172863                    2.924968   \n..                                ...                         ...   \n108                          0.881893                    6.214588   \n109                          0.772507                    6.476395   \n110                          0.692462                   -0.703198   \n111                         -0.000685                    4.610058   \n112                          0.692462                    8.818567   \n\n     log_weaning_weight_g  log_adult_body_mass_g  log_female_body_mass_g  \\\n0                4.700480               6.802117                7.013985   \n1                7.570443              10.831270                9.846167   \n2                6.129428               6.673336                7.444762   \n3               14.492895              15.830414               10.918308   \n4                5.166991                    NaN                5.248068   \n..                    ...                    ...                     ...   \n108             10.165852              12.390475               10.785992   \n109              9.784141              12.825853               11.118941   \n110              8.648221              10.156190                9.974803   \n111              7.242082               8.429520                8.992424   \n112             10.126631              11.974124               11.608236   \n\n     log_male_body_mass_g  tnc_ecoregion_breadth  log_range_size  \\\n0                7.444249              27.000000       16.494735   \n1               10.988802               0.309524       15.448508   \n2                6.720834               0.083333       13.107712   \n3               14.592295               0.559524       18.728611   \n4                6.018212               0.047619       13.059399   \n..                    ...                    ...             ...   \n108             12.105700               0.500000       17.405000   \n109             12.964539               0.166667       16.933700   \n110             11.353427               0.095238       12.645709   \n111              8.741935               0.619048       18.097751   \n112             12.873902               0.047619       14.314292   \n\n     X2.1_AgeatEyeOpening_d  X9.1_GestationLen_d  X10.2_SocialGrpSize  \\\n0                       NaN            36.705987                  NaN   \n1                      9.03            92.240000                  NaN   \n2                       NaN           108.672743                  4.0   \n3                       NaN           304.160000                  2.0   \n4                       NaN           108.754439                  NaN   \n..                      ...                  ...                  ...   \n108                   31.82           227.560000                  NaN   \n109                   34.12            64.660000                  NaN   \n110                     NaN            30.000000                  NaN   \n111                   14.01            52.500000                  NaN   \n112                    0.00           349.990000                  NaN   \n\n     X24.1_TeatNumber  X6.2_TrophicLevel  X13.3_WeaningHeadBodyLen_mm  \\\n0                 NaN                NaN                          NaN   \n1                12.0                3.0                          NaN   \n2                 NaN                2.0                          NaN   \n3                 NaN                3.0                          NaN   \n4                 NaN                NaN                          NaN   \n..                ...                ...                          ...   \n108               NaN                2.0                          NaN   \n109               4.0                2.0                          NaN   \n110               NaN                1.0                          NaN   \n111               8.0                3.0                        300.0   \n112               NaN                2.0                          NaN   \n\n     X26.2_GR_MaxLat_dd  X26.3_GR_MinLat_dd  X26.4_GR_MidRangeLat_dd  \\\n0             71.390000           24.940000                48.160000   \n1             37.720000          -29.220000                 4.240000   \n2             -2.720000           -6.470000                -4.590000   \n3             11.659394            0.560051                 6.201413   \n4             13.695438           -0.794176                 6.201413   \n..                  ...                 ...                      ...   \n108           72.380000           29.140000                50.760000   \n109           10.828224           -0.169243                 6.201413   \n110          -28.470000          -43.640000               -36.050000   \n111           74.040000           12.160000                43.100000   \n112           13.860632           -0.732860                 6.201413   \n\n     X26.5_GR_MaxLong_dd  X26.6_GR_MinLong_dd  X26.7_GR_MidRangeLong_dd  \\\n0             -55.610000          -168.120000               -111.870000   \n1              60.820000           -14.910000                 22.950000   \n2             -66.610000           -75.950000                -71.280000   \n3              35.846219            14.805832                 25.230358   \n4              35.846219            14.805832                 25.230358   \n..                   ...                  ...                       ...   \n108           179.990000          -168.120000                  5.930000   \n109            35.846219            14.805832                 25.230358   \n110           152.320000           138.710000                145.510000   \n111           179.990000          -179.990000                  0.000000   \n112            35.846219            14.805832                 25.230358   \n\n     X27.4_HuPopDen_Change  X28.1_Precip_Mean_mm  X28.2_Temp_Mean_01degC  \\\n0                 0.030000             55.370000               14.910000   \n1                 0.110000             51.260000              230.830000   \n2                 0.050000            228.040000              261.240000   \n3                      NaN            148.434830              177.854437   \n4                      NaN            149.293170              181.553030   \n..                     ...                   ...                     ...   \n108               0.004331             35.010000              -46.560000   \n109                    NaN            149.443347              182.136850   \n110               0.010000             64.510000              129.650000   \n111               0.040000             46.580000               34.320000   \n112                    NaN            149.336948              181.948970   \n\n     X30.1_AET_Mean_mm  X30.2_PET_Mean_mm  log_DispersalAge_d  \\\n0           458.260000         529.250000                 NaN   \n1           595.060000        1499.040000            6.142037   \n2          1531.250000        1533.130000                 NaN   \n3           894.442357        1307.267867                 NaN   \n4           875.991850        1326.443988                 NaN   \n..                 ...                ...                 ...   \n108         292.870000         408.250000            6.816177   \n109         866.298574        1307.923217            6.710511   \n110         610.730000         973.380000                 NaN   \n111         407.980000         646.290000            5.416100   \n112         851.319282        1328.334542                 NaN   \n\n     log_HomeRange_km2  log_HomeRange_Indiv_km2  log_PopulationDensity_n.km2  \\\n0            -2.866854                -2.754329                     4.085635   \n1             4.480400                 4.547329                    -4.605170   \n2            -2.939557                -2.663378                     3.337903   \n3             2.442211                -2.458595                    -1.034633   \n4            -1.319838                -2.781916                     5.082738   \n..                 ...                      ...                          ...   \n108           5.739182                 5.810602                    -3.912023   \n109          11.255067                11.326481                    -4.954954   \n110          -2.525729                -2.659260                     3.255015   \n111           1.252763                 1.255616                     0.095310   \n112           2.291362                -2.322158                    -0.693147   \n\n     log_PopulationGrpSize  log_HuPopDen_Min_n.km2  log_HuPopDen_Mean_n.km2  \\\n0                      NaN                1.056682                 3.006178   \n1                      NaN                0.907587                 2.989714   \n2                      NaN                1.669032                 0.122218   \n3                      0.0                1.632281                 1.625241   \n4                      NaN                1.847101                 1.977512   \n..                     ...                     ...                      ...   \n108                    NaN                0.923684                 2.698000   \n109                    0.0                1.641823                 1.700675   \n110                    0.0                0.189470                 3.266141   \n111                    NaN                0.481049                 4.196299   \n112                    NaN                1.600494                 1.707005   \n\n     log_HuPopDen_5p_n.km2  log_NeonateHeadBodyLen_mm  log_WOS_hits  \\\n0                 0.025741                   3.803203      7.557473   \n1                -0.067474                   5.521461      6.694562   \n2                 0.455452                   4.046583      4.700481   \n3                 0.654467                   8.935902      6.159096   \n4                 0.721810                   3.975580      3.663564   \n..                     ...                        ...           ...   \n108              -0.059683                   6.110936      7.664347   \n109               0.589617                   6.116348      7.138867   \n110               0.000000                   3.090946      5.111988   \n111              -0.072112                   5.356510      8.300529   \n112               0.746955                   6.534284      6.870054   \n\n     Afrosoricida  Artiodactyla  Carnivora  Cetacea  Chiroptera  Cingulata  \\\n0               0             0          1        0           0          0   \n1               0             0          1        0           0          0   \n2               0             0          0        0           0          0   \n3               0             0          0        1           0          0   \n4               0             1          0        0           0          0   \n..            ...           ...        ...      ...         ...        ...   \n108             0             0          1        0           0          0   \n109             0             0          1        0           0          0   \n110             0             0          0        0           0          0   \n111             0             0          1        0           0          0   \n112             0             0          1        0           0          0   \n\n     Dasyuromorphia  Didelphimorphia  Diprotodontia  Erinaceomorpha  \\\n0                 0                0              0               0   \n1                 0                0              0               0   \n2                 0                0              0               0   \n3                 0                0              0               0   \n4                 0                0              0               0   \n..              ...              ...            ...             ...   \n108               0                0              0               0   \n109               0                0              0               0   \n110               0                0              1               0   \n111               0                0              0               0   \n112               0                0              0               0   \n\n     Lagomorpha  Macroscelidea  Monotremata  Perissodactyla  Pholidota  \\\n0             0              0            0               0          0   \n1             0              0            0               0          0   \n2             0              0            0               0          0   \n3             0              0            0               0          0   \n4             0              0            0               0          0   \n..          ...            ...          ...             ...        ...   \n108           0              0            0               0          0   \n109           0              0            0               0          0   \n110           0              0            0               0          0   \n111           0              0            0               0          0   \n112           0              0            0               0          0   \n\n     Primates  Proboscidea  Rodentia  Sirenia  Soricomorpha  Tubulidentata  \\\n0           0            0         0        0             0              0   \n1           0            0         0        0             0              0   \n2           1            0         0        0             0              0   \n3           0            0         0        0             0              0   \n4           0            0         0        0             0              0   \n..        ...          ...       ...      ...           ...            ...   \n108         0            0         0        0             0              0   \n109         0            0         0        0             0              0   \n110         0            0         0        0             0              0   \n111         0            0         0        0             0              0   \n112         0            0         0        0             0              0   \n\n     nchar  haddock_score_mean  haddock_score_sd  AA_83_Y  AA_30_negative  \\\n0      805         -144.814760          1.580668        1               1   \n1      805         -127.078110          1.747960        1               1   \n2      805         -140.619180          2.629311        1               1   \n3      804         -117.739420          2.003812        1               0   \n4      804         -128.363990          2.843593        1               1   \n..     ...                 ...               ...      ...             ...   \n108    805         -164.694640          3.378159        1               1   \n109    790         -148.124260          2.376873        1               1   \n110    809         -125.032739          3.420254        0               1   \n111    804         -141.373520          4.313306        1               1   \n112    806         -129.135090          3.579590        1               1   \n\n     binary_haddock_score  \n0                       1  \n1                       0  \n2                       1  \n3                       0  \n4                       0  \n..                    ...  \n108                     1  \n109                     1  \n110                     0  \n111                     1  \n112                     1  \n\n[113 rows x 82 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Species</th>\n      <th>ForStrat_terrestrial</th>\n      <th>ForStrat_aquatic</th>\n      <th>Activity.Nocturnal</th>\n      <th>Activity.Crepuscular</th>\n      <th>Activity.Diurnal</th>\n      <th>female_maturity_d</th>\n      <th>male_maturity_d</th>\n      <th>weaning_d</th>\n      <th>litters_or_clutches_per_y</th>\n      <th>longevity_y</th>\n      <th>adult_svl_cm</th>\n      <th>infantMortalityRate_per_year</th>\n      <th>mortalityRateDoublingTime_y</th>\n      <th>metabolicRate_W</th>\n      <th>temperature_K</th>\n      <th>diet_breadth</th>\n      <th>development_d</th>\n      <th>mass_specific_production</th>\n      <th>log_litterclutch_size_n</th>\n      <th>log_inter_litterbirth_interval_y</th>\n      <th>log_birthhatching_weight_g</th>\n      <th>log_weaning_weight_g</th>\n      <th>log_adult_body_mass_g</th>\n      <th>log_female_body_mass_g</th>\n      <th>log_male_body_mass_g</th>\n      <th>tnc_ecoregion_breadth</th>\n      <th>log_range_size</th>\n      <th>X2.1_AgeatEyeOpening_d</th>\n      <th>X9.1_GestationLen_d</th>\n      <th>X10.2_SocialGrpSize</th>\n      <th>X24.1_TeatNumber</th>\n      <th>X6.2_TrophicLevel</th>\n      <th>X13.3_WeaningHeadBodyLen_mm</th>\n      <th>X26.2_GR_MaxLat_dd</th>\n      <th>X26.3_GR_MinLat_dd</th>\n      <th>X26.4_GR_MidRangeLat_dd</th>\n      <th>X26.5_GR_MaxLong_dd</th>\n      <th>X26.6_GR_MinLong_dd</th>\n      <th>X26.7_GR_MidRangeLong_dd</th>\n      <th>X27.4_HuPopDen_Change</th>\n      <th>X28.1_Precip_Mean_mm</th>\n      <th>X28.2_Temp_Mean_01degC</th>\n      <th>X30.1_AET_Mean_mm</th>\n      <th>X30.2_PET_Mean_mm</th>\n      <th>log_DispersalAge_d</th>\n      <th>log_HomeRange_km2</th>\n      <th>log_HomeRange_Indiv_km2</th>\n      <th>log_PopulationDensity_n.km2</th>\n      <th>log_PopulationGrpSize</th>\n      <th>log_HuPopDen_Min_n.km2</th>\n      <th>log_HuPopDen_Mean_n.km2</th>\n      <th>log_HuPopDen_5p_n.km2</th>\n      <th>log_NeonateHeadBodyLen_mm</th>\n      <th>log_WOS_hits</th>\n      <th>Afrosoricida</th>\n      <th>Artiodactyla</th>\n      <th>Carnivora</th>\n      <th>Cetacea</th>\n      <th>Chiroptera</th>\n      <th>Cingulata</th>\n      <th>Dasyuromorphia</th>\n      <th>Didelphimorphia</th>\n      <th>Diprotodontia</th>\n      <th>Erinaceomorpha</th>\n      <th>Lagomorpha</th>\n      <th>Macroscelidea</th>\n      <th>Monotremata</th>\n      <th>Perissodactyla</th>\n      <th>Pholidota</th>\n      <th>Primates</th>\n      <th>Proboscidea</th>\n      <th>Rodentia</th>\n      <th>Sirenia</th>\n      <th>Soricomorpha</th>\n      <th>Tubulidentata</th>\n      <th>nchar</th>\n      <th>haddock_score_mean</th>\n      <th>haddock_score_sd</th>\n      <th>AA_83_Y</th>\n      <th>AA_30_negative</th>\n      <th>binary_haddock_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Neovison vison</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>297.722620</td>\n      <td>371.000000</td>\n      <td>49.000000</td>\n      <td>1.000000</td>\n      <td>10.695833</td>\n      <td>43.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.722</td>\n      <td>312.15</td>\n      <td>0.4</td>\n      <td>36.220620</td>\n      <td>0.045709</td>\n      <td>1.560248</td>\n      <td>-0.000685</td>\n      <td>2.156403</td>\n      <td>4.700480</td>\n      <td>6.802117</td>\n      <td>7.013985</td>\n      <td>7.444249</td>\n      <td>27.000000</td>\n      <td>16.494735</td>\n      <td>NaN</td>\n      <td>36.705987</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>71.390000</td>\n      <td>24.940000</td>\n      <td>48.160000</td>\n      <td>-55.610000</td>\n      <td>-168.120000</td>\n      <td>-111.870000</td>\n      <td>0.030000</td>\n      <td>55.370000</td>\n      <td>14.910000</td>\n      <td>458.260000</td>\n      <td>529.250000</td>\n      <td>NaN</td>\n      <td>-2.866854</td>\n      <td>-2.754329</td>\n      <td>4.085635</td>\n      <td>NaN</td>\n      <td>1.056682</td>\n      <td>3.006178</td>\n      <td>0.025741</td>\n      <td>3.803203</td>\n      <td>7.557473</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>805</td>\n      <td>-144.814760</td>\n      <td>1.580668</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Acinonyx jubatus</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>598.985000</td>\n      <td>456.000000</td>\n      <td>107.000000</td>\n      <td>0.700000</td>\n      <td>19.000000</td>\n      <td>149.021500</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>61.770</td>\n      <td>312.15</td>\n      <td>0.1</td>\n      <td>89.060000</td>\n      <td>0.019578</td>\n      <td>1.111858</td>\n      <td>0.395145</td>\n      <td>6.142747</td>\n      <td>7.570443</td>\n      <td>10.831270</td>\n      <td>9.846167</td>\n      <td>10.988802</td>\n      <td>0.309524</td>\n      <td>15.448508</td>\n      <td>9.03</td>\n      <td>92.240000</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>37.720000</td>\n      <td>-29.220000</td>\n      <td>4.240000</td>\n      <td>60.820000</td>\n      <td>-14.910000</td>\n      <td>22.950000</td>\n      <td>0.110000</td>\n      <td>51.260000</td>\n      <td>230.830000</td>\n      <td>595.060000</td>\n      <td>1499.040000</td>\n      <td>6.142037</td>\n      <td>4.480400</td>\n      <td>4.547329</td>\n      <td>-4.605170</td>\n      <td>NaN</td>\n      <td>0.907587</td>\n      <td>2.989714</td>\n      <td>-0.067474</td>\n      <td>5.521461</td>\n      <td>6.694562</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>805</td>\n      <td>-127.078110</td>\n      <td>1.747960</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Aotus nancymaae</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>683.548198</td>\n      <td>627.229743</td>\n      <td>199.718698</td>\n      <td>1.373433</td>\n      <td>11.391667</td>\n      <td>37.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.6</td>\n      <td>127.745221</td>\n      <td>0.153402</td>\n      <td>0.019803</td>\n      <td>-0.116040</td>\n      <td>4.275836</td>\n      <td>6.129428</td>\n      <td>6.673336</td>\n      <td>7.444762</td>\n      <td>6.720834</td>\n      <td>0.083333</td>\n      <td>13.107712</td>\n      <td>NaN</td>\n      <td>108.672743</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>-2.720000</td>\n      <td>-6.470000</td>\n      <td>-4.590000</td>\n      <td>-66.610000</td>\n      <td>-75.950000</td>\n      <td>-71.280000</td>\n      <td>0.050000</td>\n      <td>228.040000</td>\n      <td>261.240000</td>\n      <td>1531.250000</td>\n      <td>1533.130000</td>\n      <td>NaN</td>\n      <td>-2.939557</td>\n      <td>-2.663378</td>\n      <td>3.337903</td>\n      <td>NaN</td>\n      <td>1.669032</td>\n      <td>0.122218</td>\n      <td>0.455452</td>\n      <td>4.046583</td>\n      <td>4.700481</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>805</td>\n      <td>-140.619180</td>\n      <td>2.629311</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Balaenoptera acutorostrata</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2740.000000</td>\n      <td>2740.000000</td>\n      <td>167.340000</td>\n      <td>1.000000</td>\n      <td>48.500000</td>\n      <td>786.465500</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.2</td>\n      <td>309.306350</td>\n      <td>0.042667</td>\n      <td>0.000000</td>\n      <td>0.252002</td>\n      <td>12.676076</td>\n      <td>14.492895</td>\n      <td>15.830414</td>\n      <td>10.918308</td>\n      <td>14.592295</td>\n      <td>0.559524</td>\n      <td>18.728611</td>\n      <td>NaN</td>\n      <td>304.160000</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>11.659394</td>\n      <td>0.560051</td>\n      <td>6.201413</td>\n      <td>35.846219</td>\n      <td>14.805832</td>\n      <td>25.230358</td>\n      <td>NaN</td>\n      <td>148.434830</td>\n      <td>177.854437</td>\n      <td>894.442357</td>\n      <td>1307.267867</td>\n      <td>NaN</td>\n      <td>2.442211</td>\n      <td>-2.458595</td>\n      <td>-1.034633</td>\n      <td>0.0</td>\n      <td>1.632281</td>\n      <td>1.625241</td>\n      <td>0.654467</td>\n      <td>8.935902</td>\n      <td>6.159096</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>804</td>\n      <td>-117.739420</td>\n      <td>2.003812</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bos mutus</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>494.972661</td>\n      <td>702.981099</td>\n      <td>90.199617</td>\n      <td>1.068823</td>\n      <td>11.245579</td>\n      <td>38.234507</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>120.038185</td>\n      <td>0.407257</td>\n      <td>0.526610</td>\n      <td>-0.172863</td>\n      <td>2.924968</td>\n      <td>5.166991</td>\n      <td>NaN</td>\n      <td>5.248068</td>\n      <td>6.018212</td>\n      <td>0.047619</td>\n      <td>13.059399</td>\n      <td>NaN</td>\n      <td>108.754439</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>13.695438</td>\n      <td>-0.794176</td>\n      <td>6.201413</td>\n      <td>35.846219</td>\n      <td>14.805832</td>\n      <td>25.230358</td>\n      <td>NaN</td>\n      <td>149.293170</td>\n      <td>181.553030</td>\n      <td>875.991850</td>\n      <td>1326.443988</td>\n      <td>NaN</td>\n      <td>-1.319838</td>\n      <td>-2.781916</td>\n      <td>5.082738</td>\n      <td>NaN</td>\n      <td>1.847101</td>\n      <td>1.977512</td>\n      <td>0.721810</td>\n      <td>3.975580</td>\n      <td>3.663564</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>804</td>\n      <td>-128.363990</td>\n      <td>2.843593</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>Ursus arctos</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1320.475000</td>\n      <td>1313.000000</td>\n      <td>213.000000</td>\n      <td>0.400000</td>\n      <td>43.500000</td>\n      <td>228.500000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>310.45</td>\n      <td>0.4</td>\n      <td>103.000000</td>\n      <td>0.001888</td>\n      <td>0.819780</td>\n      <td>0.881893</td>\n      <td>6.214588</td>\n      <td>10.165852</td>\n      <td>12.390475</td>\n      <td>10.785992</td>\n      <td>12.105700</td>\n      <td>0.500000</td>\n      <td>17.405000</td>\n      <td>31.82</td>\n      <td>227.560000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>72.380000</td>\n      <td>29.140000</td>\n      <td>50.760000</td>\n      <td>179.990000</td>\n      <td>-168.120000</td>\n      <td>5.930000</td>\n      <td>0.004331</td>\n      <td>35.010000</td>\n      <td>-46.560000</td>\n      <td>292.870000</td>\n      <td>408.250000</td>\n      <td>6.816177</td>\n      <td>5.739182</td>\n      <td>5.810602</td>\n      <td>-3.912023</td>\n      <td>NaN</td>\n      <td>0.923684</td>\n      <td>2.698000</td>\n      <td>-0.059683</td>\n      <td>6.110936</td>\n      <td>7.664347</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>805</td>\n      <td>-164.694640</td>\n      <td>3.378159</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>Ursus maritimus</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1734.000000</td>\n      <td>1734.000000</td>\n      <td>326.440000</td>\n      <td>0.400000</td>\n      <td>43.775000</td>\n      <td>224.627500</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>309.95</td>\n      <td>0.2</td>\n      <td>106.207970</td>\n      <td>0.001160</td>\n      <td>0.506818</td>\n      <td>0.772507</td>\n      <td>6.476395</td>\n      <td>9.784141</td>\n      <td>12.825853</td>\n      <td>11.118941</td>\n      <td>12.964539</td>\n      <td>0.166667</td>\n      <td>16.933700</td>\n      <td>34.12</td>\n      <td>64.660000</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>10.828224</td>\n      <td>-0.169243</td>\n      <td>6.201413</td>\n      <td>35.846219</td>\n      <td>14.805832</td>\n      <td>25.230358</td>\n      <td>NaN</td>\n      <td>149.443347</td>\n      <td>182.136850</td>\n      <td>866.298574</td>\n      <td>1307.923217</td>\n      <td>6.710511</td>\n      <td>11.255067</td>\n      <td>11.326481</td>\n      <td>-4.954954</td>\n      <td>0.0</td>\n      <td>1.641823</td>\n      <td>1.700675</td>\n      <td>0.589617</td>\n      <td>6.116348</td>\n      <td>7.138867</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>790</td>\n      <td>-148.124260</td>\n      <td>2.376873</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>Vombatus ursinus</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>742.575000</td>\n      <td>730.000000</td>\n      <td>398.725000</td>\n      <td>0.770000</td>\n      <td>26.100000</td>\n      <td>109.339000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.1</td>\n      <td>27.750000</td>\n      <td>0.000015</td>\n      <td>0.000000</td>\n      <td>0.692462</td>\n      <td>-0.703198</td>\n      <td>8.648221</td>\n      <td>10.156190</td>\n      <td>9.974803</td>\n      <td>11.353427</td>\n      <td>0.095238</td>\n      <td>12.645709</td>\n      <td>NaN</td>\n      <td>30.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>-28.470000</td>\n      <td>-43.640000</td>\n      <td>-36.050000</td>\n      <td>152.320000</td>\n      <td>138.710000</td>\n      <td>145.510000</td>\n      <td>0.010000</td>\n      <td>64.510000</td>\n      <td>129.650000</td>\n      <td>610.730000</td>\n      <td>973.380000</td>\n      <td>NaN</td>\n      <td>-2.525729</td>\n      <td>-2.659260</td>\n      <td>3.255015</td>\n      <td>0.0</td>\n      <td>0.189470</td>\n      <td>3.266141</td>\n      <td>0.000000</td>\n      <td>3.090946</td>\n      <td>5.111988</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>809</td>\n      <td>-125.032739</td>\n      <td>3.420254</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>Vulpes vulpes</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>304.000000</td>\n      <td>304.000000</td>\n      <td>49.710000</td>\n      <td>1.000000</td>\n      <td>15.000000</td>\n      <td>63.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>13.731</td>\n      <td>311.85</td>\n      <td>0.3</td>\n      <td>52.176250</td>\n      <td>0.103829</td>\n      <td>1.554454</td>\n      <td>-0.000685</td>\n      <td>4.610058</td>\n      <td>7.242082</td>\n      <td>8.429520</td>\n      <td>8.992424</td>\n      <td>8.741935</td>\n      <td>0.619048</td>\n      <td>18.097751</td>\n      <td>14.01</td>\n      <td>52.500000</td>\n      <td>NaN</td>\n      <td>8.0</td>\n      <td>3.0</td>\n      <td>300.0</td>\n      <td>74.040000</td>\n      <td>12.160000</td>\n      <td>43.100000</td>\n      <td>179.990000</td>\n      <td>-179.990000</td>\n      <td>0.000000</td>\n      <td>0.040000</td>\n      <td>46.580000</td>\n      <td>34.320000</td>\n      <td>407.980000</td>\n      <td>646.290000</td>\n      <td>5.416100</td>\n      <td>1.252763</td>\n      <td>1.255616</td>\n      <td>0.095310</td>\n      <td>NaN</td>\n      <td>0.481049</td>\n      <td>4.196299</td>\n      <td>-0.072112</td>\n      <td>5.356510</td>\n      <td>8.300529</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>804</td>\n      <td>-141.373520</td>\n      <td>4.313306</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>Zalophus californianus</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1643.625000</td>\n      <td>1826.000000</td>\n      <td>315.000000</td>\n      <td>0.890000</td>\n      <td>30.000000</td>\n      <td>225.948500</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>311.25</td>\n      <td>0.2</td>\n      <td>296.920000</td>\n      <td>0.041720</td>\n      <td>0.095310</td>\n      <td>0.692462</td>\n      <td>8.818567</td>\n      <td>10.126631</td>\n      <td>11.974124</td>\n      <td>11.608236</td>\n      <td>12.873902</td>\n      <td>0.047619</td>\n      <td>14.314292</td>\n      <td>0.00</td>\n      <td>349.990000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>13.860632</td>\n      <td>-0.732860</td>\n      <td>6.201413</td>\n      <td>35.846219</td>\n      <td>14.805832</td>\n      <td>25.230358</td>\n      <td>NaN</td>\n      <td>149.336948</td>\n      <td>181.948970</td>\n      <td>851.319282</td>\n      <td>1328.334542</td>\n      <td>NaN</td>\n      <td>2.291362</td>\n      <td>-2.322158</td>\n      <td>-0.693147</td>\n      <td>NaN</td>\n      <td>1.600494</td>\n      <td>1.707005</td>\n      <td>0.746955</td>\n      <td>6.534284</td>\n      <td>6.870054</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>806</td>\n      <td>-129.135090</td>\n      <td>3.579590</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>113 rows × 82 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# binarize haddock score column if the score is lower than (-129) then 1 else 0\n",
    "# 1 == higher binding strength \n",
    "data['binary_haddock_score'] = np.where(data['haddock_score_mean'] < -129, 1, 0)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:50.321605604Z",
     "start_time": "2023-10-05T18:31:50.136120657Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "binary_haddock_score\n1    58\n0    55\nName: count, dtype: int64"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data is balanced\n",
    "data['binary_haddock_score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:50.321975994Z",
     "start_time": "2023-10-05T18:31:50.183913198Z"
    }
   },
   "outputs": [],
   "source": [
    "# dropping columns because of missing values + species\n",
    "data = data.drop(['Species', 'haddock_score_mean','infantMortalityRate_per_year',\n",
    "                  'mortalityRateDoublingTime_y','log_DispersalAge_d','log_PopulationGrpSize'],\n",
    "                  axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:50.322623114Z",
     "start_time": "2023-10-05T18:31:50.184342721Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length: (mean) 76\n"
     ]
    },
    {
     "data": {
      "text/plain": "[0.8715596330275229,\n 0.12844036697247707,\n 0.6972477064220184,\n 0.29357798165137616,\n 0.46788990825688076,\n 1022.4766558780318,\n 1150.8019466599958,\n 215.5295503059473,\n 1.3084835457397594,\n 22.554447435976094,\n 112.54815991101373,\n 15.334944186046512,\n 309.61481481481485,\n 0.23027522935779818,\n 143.9468608816732,\n 0.2762712166886184,\n 0.5995354397634647,\n -0.1275854130978098,\n 4.332207483844622,\n 6.323244886114043,\n 7.562294277692722,\n 7.177819005312888,\n 7.671922700331731,\n 0.7489462262381141,\n 14.477926392575434,\n 10.58736842105263,\n 147.68844382968012,\n 16.085,\n 5.576923076923077,\n 2.215909090909091,\n 1949.7475,\n 23.49902186035525,\n -1.6568608199943715,\n 10.90143810435817,\n 45.5739207088634,\n 0.029417225761896305,\n 22.781842724153016,\n 0.08024719595555556,\n 106.22269750992217,\n 167.47424755984085,\n 840.8234626167578,\n 1235.888836312819,\n -0.790882760426764,\n -1.435529268897314,\n 3.10307739883547,\n 0.8957156898813204,\n 3.4211319044112574,\n 0.8015399200716836,\n 4.744589341937948,\n 5.681637549589959,\n 0.017699115044247787,\n 0.035398230088495575,\n 0.18584070796460178,\n 0.08849557522123894,\n 0.168141592920354,\n 0.008849557522123894,\n 0.008849557522123894,\n 0.008849557522123894,\n 0.017699115044247787,\n 0.008849557522123894,\n 0.017699115044247787,\n 0.008849557522123894,\n 0.008849557522123894,\n 0.017699115044247787,\n 0.008849557522123894,\n 0.20353982300884957,\n 0.008849557522123894,\n 0.1415929203539823,\n 0.008849557522123894,\n 0.017699115044247787,\n 0.008849557522123894,\n 803.0973451327434,\n 2.4672619743362847,\n 0.8141592920353983,\n 0.831858407079646,\n 0.5132743362831859]"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detecting outliers with mean and standard deviation\n",
    "mean, std = [], []\n",
    "for col in data.columns:\n",
    "    mean.append(np.mean(data[col]))\n",
    "    std.append(np.std(data[col]))\n",
    "print('length: (mean)', len(mean))#, mean.shape)\n",
    "mean # mean of each attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:50.323626449Z",
     "start_time": "2023-10-05T18:31:50.184740054Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     ForStrat_terrestrial  ForStrat_aquatic  Activity.Nocturnal  \\\n0                     1.0               0.0                 1.0   \n1                     1.0               0.0                 0.0   \n2                     1.0               0.0                 1.0   \n3                     0.0               1.0                 0.0   \n4                     NaN               NaN                 NaN   \n..                    ...               ...                 ...   \n108                   1.0               0.0                 0.0   \n109                   1.0               0.0                 0.0   \n110                   1.0               0.0                 1.0   \n111                   1.0               0.0                 1.0   \n112                   0.0               1.0                 1.0   \n\n     Activity.Crepuscular  Activity.Diurnal  female_maturity_d  \\\n0                     1.0               0.0         297.722620   \n1                     0.0               1.0         598.985000   \n2                     0.0               0.0         683.548198   \n3                     0.0               1.0        2740.000000   \n4                     NaN               NaN         494.972661   \n..                    ...               ...                ...   \n108                   1.0               1.0        1320.475000   \n109                   1.0               1.0        1734.000000   \n110                   0.0               0.0         742.575000   \n111                   1.0               0.0         304.000000   \n112                   1.0               0.0        1643.625000   \n\n     male_maturity_d   weaning_d  litters_or_clutches_per_y  longevity_y  \\\n0         371.000000   49.000000                   1.000000    10.695833   \n1         456.000000  107.000000                   0.700000    19.000000   \n2         627.229743  199.718698                   1.373433    11.391667   \n3        2740.000000  167.340000                   1.000000    48.500000   \n4         702.981099   90.199617                   1.068823    11.245579   \n..               ...         ...                        ...          ...   \n108      1313.000000  213.000000                   0.400000    43.500000   \n109      1734.000000  326.440000                   0.400000    43.775000   \n110       730.000000  398.725000                   0.770000    26.100000   \n111       304.000000   49.710000                   1.000000    15.000000   \n112      1826.000000  315.000000                   0.890000    30.000000   \n\n     adult_svl_cm  metabolicRate_W  temperature_K  diet_breadth  \\\n0       43.000000            2.722         312.15           0.4   \n1      149.021500           61.770         312.15           0.1   \n2       37.000000              NaN            NaN           0.6   \n3             NaN              NaN            NaN           0.2   \n4       38.234507              NaN            NaN           NaN   \n..            ...              ...            ...           ...   \n108    228.500000              NaN         310.45           0.4   \n109    224.627500              NaN         309.95           0.2   \n110    109.339000              NaN            NaN           0.1   \n111     63.000000           13.731         311.85           0.3   \n112    225.948500              NaN         311.25           0.2   \n\n     development_d  mass_specific_production  log_litterclutch_size_n  \\\n0        36.220620                  0.045709                 1.560248   \n1        89.060000                  0.019578                 1.111858   \n2       127.745221                  0.153402                 0.019803   \n3       309.306350                  0.042667                 0.000000   \n4       120.038185                  0.407257                 0.526610   \n..             ...                       ...                      ...   \n108     103.000000                  0.001888                 0.819780   \n109     106.207970                  0.001160                 0.506818   \n110      27.750000                  0.000015                 0.000000   \n111      52.176250                  0.103829                 1.554454   \n112     296.920000                  0.041720                 0.095310   \n\n     log_inter_litterbirth_interval_y  log_birthhatching_weight_g  \\\n0                           -0.000685                    2.156403   \n1                            0.395145                    6.142747   \n2                           -0.116040                    4.275836   \n3                            0.252002                   12.676076   \n4                           -0.172863                    2.924968   \n..                                ...                         ...   \n108                          0.881893                    6.214588   \n109                          0.772507                    6.476395   \n110                          0.692462                   -0.703198   \n111                         -0.000685                    4.610058   \n112                          0.692462                    8.818567   \n\n     log_weaning_weight_g  log_adult_body_mass_g  log_female_body_mass_g  \\\n0                4.700480               6.802117                7.013985   \n1                7.570443              10.831270                9.846167   \n2                6.129428               6.673336                7.444762   \n3               14.492895              15.830414               10.918308   \n4                5.166991                    NaN                5.248068   \n..                    ...                    ...                     ...   \n108             10.165852              12.390475               10.785992   \n109              9.784141              12.825853               11.118941   \n110              8.648221              10.156190                9.974803   \n111              7.242082               8.429520                8.992424   \n112             10.126631              11.974124               11.608236   \n\n     log_male_body_mass_g  tnc_ecoregion_breadth  log_range_size  \\\n0                7.444249                    NaN       16.494735   \n1               10.988802               0.309524       15.448508   \n2                6.720834               0.083333       13.107712   \n3               14.592295               0.559524       18.728611   \n4                6.018212               0.047619       13.059399   \n..                    ...                    ...             ...   \n108             12.105700               0.500000       17.405000   \n109             12.964539               0.166667       16.933700   \n110             11.353427               0.095238       12.645709   \n111              8.741935               0.619048       18.097751   \n112             12.873902               0.047619       14.314292   \n\n     X2.1_AgeatEyeOpening_d  X9.1_GestationLen_d  X10.2_SocialGrpSize  \\\n0                       NaN            36.705987                  NaN   \n1                      9.03            92.240000                  NaN   \n2                       NaN           108.672743                  4.0   \n3                       NaN           304.160000                  2.0   \n4                       NaN           108.754439                  NaN   \n..                      ...                  ...                  ...   \n108                   31.82           227.560000                  NaN   \n109                   34.12            64.660000                  NaN   \n110                     NaN            30.000000                  NaN   \n111                   14.01            52.500000                  NaN   \n112                    0.00           349.990000                  NaN   \n\n     X24.1_TeatNumber  X6.2_TrophicLevel  X13.3_WeaningHeadBodyLen_mm  \\\n0                 NaN                NaN                          NaN   \n1                12.0                3.0                          NaN   \n2                 NaN                2.0                          NaN   \n3                 NaN                3.0                          NaN   \n4                 NaN                NaN                          NaN   \n..                ...                ...                          ...   \n108               NaN                2.0                          NaN   \n109               4.0                2.0                          NaN   \n110               NaN                1.0                          NaN   \n111               8.0                3.0                        300.0   \n112               NaN                2.0                          NaN   \n\n     X26.2_GR_MaxLat_dd  X26.3_GR_MinLat_dd  X26.4_GR_MidRangeLat_dd  \\\n0             71.390000           24.940000                48.160000   \n1             37.720000          -29.220000                 4.240000   \n2             -2.720000           -6.470000                -4.590000   \n3             11.659394            0.560051                 6.201413   \n4             13.695438           -0.794176                 6.201413   \n..                  ...                 ...                      ...   \n108           72.380000           29.140000                50.760000   \n109           10.828224           -0.169243                 6.201413   \n110          -28.470000          -43.640000               -36.050000   \n111           74.040000           12.160000                43.100000   \n112           13.860632           -0.732860                 6.201413   \n\n     X26.5_GR_MaxLong_dd  X26.6_GR_MinLong_dd  X26.7_GR_MidRangeLong_dd  \\\n0             -55.610000          -168.120000               -111.870000   \n1              60.820000           -14.910000                 22.950000   \n2             -66.610000           -75.950000                -71.280000   \n3              35.846219            14.805832                 25.230358   \n4              35.846219            14.805832                 25.230358   \n..                   ...                  ...                       ...   \n108           179.990000          -168.120000                  5.930000   \n109            35.846219            14.805832                 25.230358   \n110           152.320000           138.710000                145.510000   \n111           179.990000          -179.990000                  0.000000   \n112            35.846219            14.805832                 25.230358   \n\n     X27.4_HuPopDen_Change  X28.1_Precip_Mean_mm  X28.2_Temp_Mean_01degC  \\\n0                 0.030000             55.370000               14.910000   \n1                 0.110000             51.260000              230.830000   \n2                 0.050000            228.040000              261.240000   \n3                      NaN            148.434830              177.854437   \n4                      NaN            149.293170              181.553030   \n..                     ...                   ...                     ...   \n108               0.004331             35.010000              -46.560000   \n109                    NaN            149.443347              182.136850   \n110               0.010000             64.510000              129.650000   \n111               0.040000             46.580000               34.320000   \n112                    NaN            149.336948              181.948970   \n\n     X30.1_AET_Mean_mm  X30.2_PET_Mean_mm  log_HomeRange_km2  \\\n0           458.260000         529.250000          -2.866854   \n1           595.060000        1499.040000           4.480400   \n2          1531.250000        1533.130000          -2.939557   \n3           894.442357        1307.267867           2.442211   \n4           875.991850        1326.443988          -1.319838   \n..                 ...                ...                ...   \n108         292.870000         408.250000           5.739182   \n109         866.298574        1307.923217                NaN   \n110         610.730000         973.380000          -2.525729   \n111         407.980000         646.290000           1.252763   \n112         851.319282        1328.334542           2.291362   \n\n     log_HomeRange_Indiv_km2  log_PopulationDensity_n.km2  \\\n0                  -2.754329                     4.085635   \n1                   4.547329                    -4.605170   \n2                  -2.663378                     3.337903   \n3                  -2.458595                    -1.034633   \n4                  -2.781916                     5.082738   \n..                       ...                          ...   \n108                 5.810602                    -3.912023   \n109                      NaN                    -4.954954   \n110                -2.659260                     3.255015   \n111                 1.255616                     0.095310   \n112                -2.322158                    -0.693147   \n\n     log_HuPopDen_Min_n.km2  log_HuPopDen_Mean_n.km2  log_HuPopDen_5p_n.km2  \\\n0                  1.056682                 3.006178               0.025741   \n1                  0.907587                 2.989714              -0.067474   \n2                  1.669032                 0.122218               0.455452   \n3                  1.632281                 1.625241               0.654467   \n4                  1.847101                 1.977512               0.721810   \n..                      ...                      ...                    ...   \n108                0.923684                 2.698000              -0.059683   \n109                1.641823                 1.700675               0.589617   \n110                0.189470                 3.266141               0.000000   \n111                0.481049                 4.196299              -0.072112   \n112                1.600494                 1.707005               0.746955   \n\n     log_NeonateHeadBodyLen_mm  log_WOS_hits  Afrosoricida  Artiodactyla  \\\n0                     3.803203      7.557473           0.0           0.0   \n1                     5.521461      6.694562           0.0           0.0   \n2                     4.046583      4.700481           0.0           0.0   \n3                          NaN      6.159096           0.0           0.0   \n4                     3.975580      3.663564           0.0           NaN   \n..                         ...           ...           ...           ...   \n108                   6.110936      7.664347           0.0           0.0   \n109                   6.116348      7.138867           0.0           0.0   \n110                   3.090946      5.111988           0.0           0.0   \n111                   5.356510      8.300529           0.0           0.0   \n112                   6.534284      6.870054           0.0           0.0   \n\n     Carnivora  Cetacea  Chiroptera  Cingulata  Dasyuromorphia  \\\n0            1      0.0           0        0.0             0.0   \n1            1      0.0           0        0.0             0.0   \n2            0      0.0           0        0.0             0.0   \n3            0      NaN           0        0.0             0.0   \n4            0      0.0           0        0.0             0.0   \n..         ...      ...         ...        ...             ...   \n108          1      0.0           0        0.0             0.0   \n109          1      0.0           0        0.0             0.0   \n110          0      0.0           0        0.0             0.0   \n111          1      0.0           0        0.0             0.0   \n112          1      0.0           0        0.0             0.0   \n\n     Didelphimorphia  Diprotodontia  Erinaceomorpha  Lagomorpha  \\\n0                0.0            0.0             0.0         0.0   \n1                0.0            0.0             0.0         0.0   \n2                0.0            0.0             0.0         0.0   \n3                0.0            0.0             0.0         0.0   \n4                0.0            0.0             0.0         0.0   \n..               ...            ...             ...         ...   \n108              0.0            0.0             0.0         0.0   \n109              0.0            0.0             0.0         0.0   \n110              0.0            NaN             0.0         0.0   \n111              0.0            0.0             0.0         0.0   \n112              0.0            0.0             0.0         0.0   \n\n     Macroscelidea  Monotremata  Perissodactyla  Pholidota  Primates  \\\n0              0.0          0.0             0.0        0.0         0   \n1              0.0          0.0             0.0        0.0         0   \n2              0.0          0.0             0.0        0.0         1   \n3              0.0          0.0             0.0        0.0         0   \n4              0.0          0.0             0.0        0.0         0   \n..             ...          ...             ...        ...       ...   \n108            0.0          0.0             0.0        0.0         0   \n109            0.0          0.0             0.0        0.0         0   \n110            0.0          0.0             0.0        0.0         0   \n111            0.0          0.0             0.0        0.0         0   \n112            0.0          0.0             0.0        0.0         0   \n\n     Proboscidea  Rodentia  Sirenia  Soricomorpha  Tubulidentata  nchar  \\\n0            0.0         0      0.0           0.0            0.0  805.0   \n1            0.0         0      0.0           0.0            0.0  805.0   \n2            0.0         0      0.0           0.0            0.0  805.0   \n3            0.0         0      0.0           0.0            0.0  804.0   \n4            0.0         0      0.0           0.0            0.0  804.0   \n..           ...       ...      ...           ...            ...    ...   \n108          0.0         0      0.0           0.0            0.0  805.0   \n109          0.0         0      0.0           0.0            0.0  790.0   \n110          0.0         0      0.0           0.0            0.0  809.0   \n111          0.0         0      0.0           0.0            0.0  804.0   \n112          0.0         0      0.0           0.0            0.0  806.0   \n\n     haddock_score_sd  AA_83_Y  AA_30_negative  binary_haddock_score  \n0            1.580668        1               1                     1  \n1            1.747960        1               1                     0  \n2            2.629311        1               1                     1  \n3            2.003812        1               0                     0  \n4            2.843593        1               1                     0  \n..                ...      ...             ...                   ...  \n108          3.378159        1               1                     1  \n109          2.376873        1               1                     1  \n110          3.420254        0               1                     0  \n111          4.313306        1               1                     1  \n112          3.579590        1               1                     1  \n\n[113 rows x 76 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ForStrat_terrestrial</th>\n      <th>ForStrat_aquatic</th>\n      <th>Activity.Nocturnal</th>\n      <th>Activity.Crepuscular</th>\n      <th>Activity.Diurnal</th>\n      <th>female_maturity_d</th>\n      <th>male_maturity_d</th>\n      <th>weaning_d</th>\n      <th>litters_or_clutches_per_y</th>\n      <th>longevity_y</th>\n      <th>adult_svl_cm</th>\n      <th>metabolicRate_W</th>\n      <th>temperature_K</th>\n      <th>diet_breadth</th>\n      <th>development_d</th>\n      <th>mass_specific_production</th>\n      <th>log_litterclutch_size_n</th>\n      <th>log_inter_litterbirth_interval_y</th>\n      <th>log_birthhatching_weight_g</th>\n      <th>log_weaning_weight_g</th>\n      <th>log_adult_body_mass_g</th>\n      <th>log_female_body_mass_g</th>\n      <th>log_male_body_mass_g</th>\n      <th>tnc_ecoregion_breadth</th>\n      <th>log_range_size</th>\n      <th>X2.1_AgeatEyeOpening_d</th>\n      <th>X9.1_GestationLen_d</th>\n      <th>X10.2_SocialGrpSize</th>\n      <th>X24.1_TeatNumber</th>\n      <th>X6.2_TrophicLevel</th>\n      <th>X13.3_WeaningHeadBodyLen_mm</th>\n      <th>X26.2_GR_MaxLat_dd</th>\n      <th>X26.3_GR_MinLat_dd</th>\n      <th>X26.4_GR_MidRangeLat_dd</th>\n      <th>X26.5_GR_MaxLong_dd</th>\n      <th>X26.6_GR_MinLong_dd</th>\n      <th>X26.7_GR_MidRangeLong_dd</th>\n      <th>X27.4_HuPopDen_Change</th>\n      <th>X28.1_Precip_Mean_mm</th>\n      <th>X28.2_Temp_Mean_01degC</th>\n      <th>X30.1_AET_Mean_mm</th>\n      <th>X30.2_PET_Mean_mm</th>\n      <th>log_HomeRange_km2</th>\n      <th>log_HomeRange_Indiv_km2</th>\n      <th>log_PopulationDensity_n.km2</th>\n      <th>log_HuPopDen_Min_n.km2</th>\n      <th>log_HuPopDen_Mean_n.km2</th>\n      <th>log_HuPopDen_5p_n.km2</th>\n      <th>log_NeonateHeadBodyLen_mm</th>\n      <th>log_WOS_hits</th>\n      <th>Afrosoricida</th>\n      <th>Artiodactyla</th>\n      <th>Carnivora</th>\n      <th>Cetacea</th>\n      <th>Chiroptera</th>\n      <th>Cingulata</th>\n      <th>Dasyuromorphia</th>\n      <th>Didelphimorphia</th>\n      <th>Diprotodontia</th>\n      <th>Erinaceomorpha</th>\n      <th>Lagomorpha</th>\n      <th>Macroscelidea</th>\n      <th>Monotremata</th>\n      <th>Perissodactyla</th>\n      <th>Pholidota</th>\n      <th>Primates</th>\n      <th>Proboscidea</th>\n      <th>Rodentia</th>\n      <th>Sirenia</th>\n      <th>Soricomorpha</th>\n      <th>Tubulidentata</th>\n      <th>nchar</th>\n      <th>haddock_score_sd</th>\n      <th>AA_83_Y</th>\n      <th>AA_30_negative</th>\n      <th>binary_haddock_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>297.722620</td>\n      <td>371.000000</td>\n      <td>49.000000</td>\n      <td>1.000000</td>\n      <td>10.695833</td>\n      <td>43.000000</td>\n      <td>2.722</td>\n      <td>312.15</td>\n      <td>0.4</td>\n      <td>36.220620</td>\n      <td>0.045709</td>\n      <td>1.560248</td>\n      <td>-0.000685</td>\n      <td>2.156403</td>\n      <td>4.700480</td>\n      <td>6.802117</td>\n      <td>7.013985</td>\n      <td>7.444249</td>\n      <td>NaN</td>\n      <td>16.494735</td>\n      <td>NaN</td>\n      <td>36.705987</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>71.390000</td>\n      <td>24.940000</td>\n      <td>48.160000</td>\n      <td>-55.610000</td>\n      <td>-168.120000</td>\n      <td>-111.870000</td>\n      <td>0.030000</td>\n      <td>55.370000</td>\n      <td>14.910000</td>\n      <td>458.260000</td>\n      <td>529.250000</td>\n      <td>-2.866854</td>\n      <td>-2.754329</td>\n      <td>4.085635</td>\n      <td>1.056682</td>\n      <td>3.006178</td>\n      <td>0.025741</td>\n      <td>3.803203</td>\n      <td>7.557473</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>805.0</td>\n      <td>1.580668</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>598.985000</td>\n      <td>456.000000</td>\n      <td>107.000000</td>\n      <td>0.700000</td>\n      <td>19.000000</td>\n      <td>149.021500</td>\n      <td>61.770</td>\n      <td>312.15</td>\n      <td>0.1</td>\n      <td>89.060000</td>\n      <td>0.019578</td>\n      <td>1.111858</td>\n      <td>0.395145</td>\n      <td>6.142747</td>\n      <td>7.570443</td>\n      <td>10.831270</td>\n      <td>9.846167</td>\n      <td>10.988802</td>\n      <td>0.309524</td>\n      <td>15.448508</td>\n      <td>9.03</td>\n      <td>92.240000</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>37.720000</td>\n      <td>-29.220000</td>\n      <td>4.240000</td>\n      <td>60.820000</td>\n      <td>-14.910000</td>\n      <td>22.950000</td>\n      <td>0.110000</td>\n      <td>51.260000</td>\n      <td>230.830000</td>\n      <td>595.060000</td>\n      <td>1499.040000</td>\n      <td>4.480400</td>\n      <td>4.547329</td>\n      <td>-4.605170</td>\n      <td>0.907587</td>\n      <td>2.989714</td>\n      <td>-0.067474</td>\n      <td>5.521461</td>\n      <td>6.694562</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>805.0</td>\n      <td>1.747960</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>683.548198</td>\n      <td>627.229743</td>\n      <td>199.718698</td>\n      <td>1.373433</td>\n      <td>11.391667</td>\n      <td>37.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.6</td>\n      <td>127.745221</td>\n      <td>0.153402</td>\n      <td>0.019803</td>\n      <td>-0.116040</td>\n      <td>4.275836</td>\n      <td>6.129428</td>\n      <td>6.673336</td>\n      <td>7.444762</td>\n      <td>6.720834</td>\n      <td>0.083333</td>\n      <td>13.107712</td>\n      <td>NaN</td>\n      <td>108.672743</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>-2.720000</td>\n      <td>-6.470000</td>\n      <td>-4.590000</td>\n      <td>-66.610000</td>\n      <td>-75.950000</td>\n      <td>-71.280000</td>\n      <td>0.050000</td>\n      <td>228.040000</td>\n      <td>261.240000</td>\n      <td>1531.250000</td>\n      <td>1533.130000</td>\n      <td>-2.939557</td>\n      <td>-2.663378</td>\n      <td>3.337903</td>\n      <td>1.669032</td>\n      <td>0.122218</td>\n      <td>0.455452</td>\n      <td>4.046583</td>\n      <td>4.700481</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>805.0</td>\n      <td>2.629311</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2740.000000</td>\n      <td>2740.000000</td>\n      <td>167.340000</td>\n      <td>1.000000</td>\n      <td>48.500000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.2</td>\n      <td>309.306350</td>\n      <td>0.042667</td>\n      <td>0.000000</td>\n      <td>0.252002</td>\n      <td>12.676076</td>\n      <td>14.492895</td>\n      <td>15.830414</td>\n      <td>10.918308</td>\n      <td>14.592295</td>\n      <td>0.559524</td>\n      <td>18.728611</td>\n      <td>NaN</td>\n      <td>304.160000</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>11.659394</td>\n      <td>0.560051</td>\n      <td>6.201413</td>\n      <td>35.846219</td>\n      <td>14.805832</td>\n      <td>25.230358</td>\n      <td>NaN</td>\n      <td>148.434830</td>\n      <td>177.854437</td>\n      <td>894.442357</td>\n      <td>1307.267867</td>\n      <td>2.442211</td>\n      <td>-2.458595</td>\n      <td>-1.034633</td>\n      <td>1.632281</td>\n      <td>1.625241</td>\n      <td>0.654467</td>\n      <td>NaN</td>\n      <td>6.159096</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>804.0</td>\n      <td>2.003812</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>494.972661</td>\n      <td>702.981099</td>\n      <td>90.199617</td>\n      <td>1.068823</td>\n      <td>11.245579</td>\n      <td>38.234507</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>120.038185</td>\n      <td>0.407257</td>\n      <td>0.526610</td>\n      <td>-0.172863</td>\n      <td>2.924968</td>\n      <td>5.166991</td>\n      <td>NaN</td>\n      <td>5.248068</td>\n      <td>6.018212</td>\n      <td>0.047619</td>\n      <td>13.059399</td>\n      <td>NaN</td>\n      <td>108.754439</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>13.695438</td>\n      <td>-0.794176</td>\n      <td>6.201413</td>\n      <td>35.846219</td>\n      <td>14.805832</td>\n      <td>25.230358</td>\n      <td>NaN</td>\n      <td>149.293170</td>\n      <td>181.553030</td>\n      <td>875.991850</td>\n      <td>1326.443988</td>\n      <td>-1.319838</td>\n      <td>-2.781916</td>\n      <td>5.082738</td>\n      <td>1.847101</td>\n      <td>1.977512</td>\n      <td>0.721810</td>\n      <td>3.975580</td>\n      <td>3.663564</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>804.0</td>\n      <td>2.843593</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1320.475000</td>\n      <td>1313.000000</td>\n      <td>213.000000</td>\n      <td>0.400000</td>\n      <td>43.500000</td>\n      <td>228.500000</td>\n      <td>NaN</td>\n      <td>310.45</td>\n      <td>0.4</td>\n      <td>103.000000</td>\n      <td>0.001888</td>\n      <td>0.819780</td>\n      <td>0.881893</td>\n      <td>6.214588</td>\n      <td>10.165852</td>\n      <td>12.390475</td>\n      <td>10.785992</td>\n      <td>12.105700</td>\n      <td>0.500000</td>\n      <td>17.405000</td>\n      <td>31.82</td>\n      <td>227.560000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>72.380000</td>\n      <td>29.140000</td>\n      <td>50.760000</td>\n      <td>179.990000</td>\n      <td>-168.120000</td>\n      <td>5.930000</td>\n      <td>0.004331</td>\n      <td>35.010000</td>\n      <td>-46.560000</td>\n      <td>292.870000</td>\n      <td>408.250000</td>\n      <td>5.739182</td>\n      <td>5.810602</td>\n      <td>-3.912023</td>\n      <td>0.923684</td>\n      <td>2.698000</td>\n      <td>-0.059683</td>\n      <td>6.110936</td>\n      <td>7.664347</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>805.0</td>\n      <td>3.378159</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1734.000000</td>\n      <td>1734.000000</td>\n      <td>326.440000</td>\n      <td>0.400000</td>\n      <td>43.775000</td>\n      <td>224.627500</td>\n      <td>NaN</td>\n      <td>309.95</td>\n      <td>0.2</td>\n      <td>106.207970</td>\n      <td>0.001160</td>\n      <td>0.506818</td>\n      <td>0.772507</td>\n      <td>6.476395</td>\n      <td>9.784141</td>\n      <td>12.825853</td>\n      <td>11.118941</td>\n      <td>12.964539</td>\n      <td>0.166667</td>\n      <td>16.933700</td>\n      <td>34.12</td>\n      <td>64.660000</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>10.828224</td>\n      <td>-0.169243</td>\n      <td>6.201413</td>\n      <td>35.846219</td>\n      <td>14.805832</td>\n      <td>25.230358</td>\n      <td>NaN</td>\n      <td>149.443347</td>\n      <td>182.136850</td>\n      <td>866.298574</td>\n      <td>1307.923217</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-4.954954</td>\n      <td>1.641823</td>\n      <td>1.700675</td>\n      <td>0.589617</td>\n      <td>6.116348</td>\n      <td>7.138867</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>790.0</td>\n      <td>2.376873</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>742.575000</td>\n      <td>730.000000</td>\n      <td>398.725000</td>\n      <td>0.770000</td>\n      <td>26.100000</td>\n      <td>109.339000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.1</td>\n      <td>27.750000</td>\n      <td>0.000015</td>\n      <td>0.000000</td>\n      <td>0.692462</td>\n      <td>-0.703198</td>\n      <td>8.648221</td>\n      <td>10.156190</td>\n      <td>9.974803</td>\n      <td>11.353427</td>\n      <td>0.095238</td>\n      <td>12.645709</td>\n      <td>NaN</td>\n      <td>30.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>-28.470000</td>\n      <td>-43.640000</td>\n      <td>-36.050000</td>\n      <td>152.320000</td>\n      <td>138.710000</td>\n      <td>145.510000</td>\n      <td>0.010000</td>\n      <td>64.510000</td>\n      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<td>49.710000</td>\n      <td>1.000000</td>\n      <td>15.000000</td>\n      <td>63.000000</td>\n      <td>13.731</td>\n      <td>311.85</td>\n      <td>0.3</td>\n      <td>52.176250</td>\n      <td>0.103829</td>\n      <td>1.554454</td>\n      <td>-0.000685</td>\n      <td>4.610058</td>\n      <td>7.242082</td>\n      <td>8.429520</td>\n      <td>8.992424</td>\n      <td>8.741935</td>\n      <td>0.619048</td>\n      <td>18.097751</td>\n      <td>14.01</td>\n      <td>52.500000</td>\n      <td>NaN</td>\n      <td>8.0</td>\n      <td>3.0</td>\n      <td>300.0</td>\n      <td>74.040000</td>\n      <td>12.160000</td>\n      <td>43.100000</td>\n      <td>179.990000</td>\n      <td>-179.990000</td>\n      <td>0.000000</td>\n      <td>0.040000</td>\n      <td>46.580000</td>\n      <td>34.320000</td>\n      <td>407.980000</td>\n      <td>646.290000</td>\n      <td>1.252763</td>\n      <td>1.255616</td>\n      <td>0.095310</td>\n      <td>0.481049</td>\n      <td>4.196299</td>\n      <td>-0.072112</td>\n      <td>5.356510</td>\n      <td>8.300529</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>804.0</td>\n      <td>4.313306</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1643.625000</td>\n      <td>1826.000000</td>\n      <td>315.000000</td>\n      <td>0.890000</td>\n      <td>30.000000</td>\n      <td>225.948500</td>\n      <td>NaN</td>\n      <td>311.25</td>\n      <td>0.2</td>\n      <td>296.920000</td>\n      <td>0.041720</td>\n      <td>0.095310</td>\n      <td>0.692462</td>\n      <td>8.818567</td>\n      <td>10.126631</td>\n      <td>11.974124</td>\n      <td>11.608236</td>\n      <td>12.873902</td>\n      <td>0.047619</td>\n      <td>14.314292</td>\n      <td>0.00</td>\n      <td>349.990000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>13.860632</td>\n      <td>-0.732860</td>\n      <td>6.201413</td>\n      <td>35.846219</td>\n      <td>14.805832</td>\n      <td>25.230358</td>\n      <td>NaN</td>\n      <td>149.336948</td>\n      <td>181.948970</td>\n      <td>851.319282</td>\n      <td>1328.334542</td>\n      <td>2.291362</td>\n      <td>-2.322158</td>\n      <td>-0.693147</td>\n      <td>1.600494</td>\n      <td>1.707005</td>\n      <td>0.746955</td>\n      <td>6.534284</td>\n      <td>6.870054</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>806.0</td>\n      <td>3.579590</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>113 rows × 76 columns</p>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing outliers\n",
    "def remove_out(df):\n",
    "    mean = df.mean()\n",
    "    std = df.std()\n",
    "    std_size = 3\n",
    "    outlier_mask = (np.abs(df - mean) <= std_size * std)\n",
    "    cleaned_df = df[outlier_mask]\n",
    "    return cleaned_df\n",
    "\n",
    "cleaned_data = remove_out(data)\n",
    "data = cleaned_data\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:50.402072009Z",
     "start_time": "2023-10-05T18:31:50.262986600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     ForStrat_terrestrial  ForStrat_aquatic  Activity.Nocturnal  \\\n0                     1.0               0.0                 1.0   \n1                     1.0               0.0                 0.0   \n2                     1.0               0.0                 1.0   \n3                     0.0               1.0                 0.0   \n4                     NaN               NaN                 NaN   \n..                    ...               ...                 ...   \n108                   1.0               0.0                 0.0   \n109                   1.0               0.0                 0.0   \n110                   1.0               0.0                 1.0   \n111                   1.0               0.0                 1.0   \n112                   0.0               1.0                 1.0   \n\n     Activity.Crepuscular  Activity.Diurnal  female_maturity_d  \\\n0                     1.0               0.0           0.065797   \n1                     0.0               1.0           0.144858   \n2                     0.0               0.0           0.167050   \n3                     0.0               1.0           0.706728   \n4                     NaN               NaN           0.117562   \n..                    ...               ...                ...   \n108                   1.0               1.0           0.334200   \n109                   1.0               1.0           0.442722   \n110                   0.0               0.0           0.182541   \n111                   1.0               0.0           0.067445   \n112                   1.0               0.0           0.419004   \n\n     male_maturity_d  weaning_d  litters_or_clutches_per_y  longevity_y  \\\n0           0.075841   0.034741                   0.210526     0.138029   \n1           0.095436   0.104223                   0.131579     0.269841   \n2           0.134908   0.215298                   0.308798     0.149074   \n3           0.621946   0.176509                   0.210526     0.738095   \n4           0.152370   0.084097                   0.228638     0.146755   \n..               ...        ...                        ...          ...   \n108         0.292992   0.231208                   0.052632     0.658730   \n109         0.390041   0.367106                   0.052632     0.663095   \n110         0.158598   0.453702                   0.150000     0.382540   \n111         0.060396   0.035592                   0.210526     0.206349   \n112         0.411249   0.353401                   0.181579     0.444444   \n\n     adult_svl_cm  metabolicRate_W  temperature_K  diet_breadth  \\\n0        0.067902         0.027137       0.864198           0.6   \n1        0.257353         0.627047       0.864198           0.0   \n2        0.057181              NaN            NaN           1.0   \n3             NaN              NaN            NaN           0.2   \n4        0.059387              NaN            NaN           NaN   \n..            ...              ...            ...           ...   \n108      0.399374              NaN       0.654321           0.6   \n109      0.392454              NaN       0.592593           0.2   \n110      0.186444              NaN            NaN           0.0   \n111      0.103641         0.138985       0.827160           0.4   \n112      0.394814              NaN       0.753086           0.2   \n\n     development_d  mass_specific_production  log_litterclutch_size_n  \\\n0         0.047435                  0.064324                 0.712741   \n1         0.164926                  0.047067                 0.510529   \n2         0.250944                  0.135447                 0.018041   \n3         0.654653                  0.062315                 0.009111   \n4         0.233807                  0.303096                 0.246598   \n..             ...                       ...                      ...   \n108       0.195922                  0.035384                 0.378810   \n109       0.203055                  0.034904                 0.237672   \n110       0.028600                  0.034147                 0.009111   \n111       0.082913                  0.102708                 0.710128   \n112       0.627112                  0.061690                 0.052093   \n\n     log_inter_litterbirth_interval_y  log_birthhatching_weight_g  \\\n0                            0.625904                    0.344187   \n1                            0.719685                    0.573453   \n2                            0.598573                    0.466081   \n3                            0.685771                    0.949202   \n4                            0.585111                    0.388389   \n..                                ...                         ...   \n108                          0.835007                    0.577584   \n109                          0.809091                    0.592642   \n110                          0.790126                    0.179724   \n111                          0.625904                    0.485304   \n112                          0.790126                    0.727346   \n\n     log_weaning_weight_g  log_adult_body_mass_g  log_female_body_mass_g  \\\n0                0.255546               0.357657                0.497357   \n1                0.473731               0.635173                0.756037   \n2                0.364180               0.348787                0.536702   \n3                1.000000               0.979500                0.853961   \n4                0.291012                    NaN                0.336066   \n..                    ...                    ...                     ...   \n108              0.671043               0.742567                0.841876   \n109              0.642024               0.772554                0.872286   \n110              0.555667               0.588676                0.767786   \n111              0.448767               0.469748                0.678059   \n112              0.668061               0.713890                0.916977   \n\n     log_male_body_mass_g  tnc_ecoregion_breadth  log_range_size  \\\n0                0.429557                    NaN        0.741971   \n1                0.687300               0.320513        0.647623   \n2                0.376954               0.076923        0.436531   \n3                0.949329               0.589744        0.943420   \n4                0.325862               0.038462        0.432175   \n..                    ...                    ...             ...   \n108              0.768516               0.525641        0.824058   \n109              0.830967               0.166667        0.781556   \n110              0.713814               0.089744        0.394868   \n111              0.523919               0.653846        0.886529   \n112              0.824376               0.038462        0.545340   \n\n     X2.1_AgeatEyeOpening_d  X9.1_GestationLen_d  X10.2_SocialGrpSize  \\\n0                       NaN             0.051922                  NaN   \n1                  0.237632             0.170946                  NaN   \n2                       NaN             0.206166             0.046875   \n3                       NaN             0.625145             0.015625   \n4                       NaN             0.206341                  NaN   \n..                      ...                  ...                  ...   \n108                0.837368             0.460971                  NaN   \n109                0.897895             0.111835                  NaN   \n110                     NaN             0.037550                  NaN   \n111                0.368684             0.085773                  NaN   \n112                0.000000             0.723370                  NaN   \n\n     X24.1_TeatNumber  X6.2_TrophicLevel  X13.3_WeaningHeadBodyLen_mm  \\\n0                 NaN                NaN                          NaN   \n1                 1.0                1.0                          NaN   \n2                 NaN                0.5                          NaN   \n3                 NaN                1.0                          NaN   \n4                 NaN                NaN                          NaN   \n..                ...                ...                          ...   \n108               NaN                0.5                          NaN   \n109               0.2                0.5                          NaN   \n110               NaN                0.0                          NaN   \n111               0.6                1.0                      0.03891   \n112               NaN                0.5                          NaN   \n\n     X26.2_GR_MaxLat_dd  X26.3_GR_MinLat_dd  X26.4_GR_MidRangeLat_dd  \\\n0              0.901577            0.818372                 0.941017   \n1              0.630613            0.250419                 0.483326   \n2              0.305167            0.488989                 0.391309   \n3              0.420887            0.562710                 0.503766   \n4              0.437272            0.548509                 0.503766   \n..                  ...                 ...                      ...   \n108            0.909545            0.862416                 0.968112   \n109            0.414198            0.555062                 0.503766   \n110            0.097940            0.099203                 0.063464   \n111            0.922904            0.684354                 0.888287   \n112            0.438602            0.549152                 0.503766   \n\n     X26.5_GR_MaxLong_dd  X26.6_GR_MinLong_dd  X26.7_GR_MidRangeLong_dd  \\\n0               0.169165             0.036599                  0.014533   \n1               0.579751             0.508595                  0.528799   \n2               0.130373             0.320548                  0.169362   \n3               0.491682             0.600141                  0.537498   \n4               0.491682             0.600141                  0.537498   \n..                   ...                  ...                       ...   \n108             1.000000             0.036599                  0.463877   \n109             0.491682             0.600141                  0.537498   \n110             0.902423             0.981855                  0.996300   \n111             1.000000             0.000031                  0.441257   \n112             0.491682             0.600141                  0.537498   \n\n     X27.4_HuPopDen_Change  X28.1_Precip_Mean_mm  X28.2_Temp_Mean_01degC  \\\n0                 0.200000              0.187966                0.194218   \n1                 0.600000              0.170364                0.876430   \n2                 0.300000              0.927452                0.972512   \n3                      NaN              0.586530                0.709050   \n4                      NaN              0.590206                0.720736   \n..                     ...                   ...                     ...   \n108               0.071655              0.100771                0.000000   \n109                    NaN              0.590849                0.722581   \n110               0.100000              0.227109                0.556746   \n111               0.250000              0.150321                0.255545   \n112                    NaN              0.590394                0.721987   \n\n     X30.1_AET_Mean_mm  X30.2_PET_Mean_mm  log_HomeRange_km2  \\\n0             0.198967           0.104034           0.369099   \n1             0.288623           0.812691           0.870614   \n2             0.902184           0.837602           0.364136   \n3             0.484833           0.672557           0.731489   \n4             0.472741           0.686570           0.474696   \n..                 ...                ...                ...   \n108           0.090574           0.015616           0.956536   \n109           0.466388           0.673036                NaN   \n110           0.298893           0.428575           0.392383   \n111           0.166015           0.189559           0.650299   \n112           0.456571           0.687951           0.721192   \n\n     log_HomeRange_Indiv_km2  log_PopulationDensity_n.km2  \\\n0                   0.349669                     0.633553   \n1                   0.828728                     0.024512   \n2                   0.355636                     0.581153   \n3                   0.369072                     0.274731   \n4                   0.347859                     0.703429   \n..                       ...                          ...   \n108                 0.911610                     0.073087   \n109                      NaN                     0.000000   \n110                 0.355906                     0.575345   \n111                 0.612760                     0.353916   \n112                 0.378023                     0.298662   \n\n     log_HuPopDen_Min_n.km2  log_HuPopDen_Mean_n.km2  log_HuPopDen_5p_n.km2  \\\n0                  0.514713                 0.411510               0.162561   \n1                  0.483852                 0.409161               0.140070   \n2                  0.641462                 0.000000               0.266244   \n3                  0.633855                 0.214465               0.314263   \n4                  0.678321                 0.264731               0.330511   \n..                      ...                      ...                    ...   \n108                0.487184                 0.367537               0.141950   \n109                0.635830                 0.225229               0.298615   \n110                0.335211                 0.448604               0.156351   \n111                0.395564                 0.581328               0.138951   \n112                0.627276                 0.226132               0.336578   \n\n     log_NeonateHeadBodyLen_mm  log_WOS_hits  Afrosoricida  Artiodactyla  \\\n0                     0.273348      0.655406           0.0           0.0   \n1                     0.565604      0.580571           0.0           0.0   \n2                     0.314744      0.407636           0.0           0.0   \n3                          NaN      0.534133           0.0           0.0   \n4                     0.302667      0.317711           0.0           NaN   \n..                         ...           ...           ...           ...   \n108                   0.665867      0.664675           0.0           0.0   \n109                   0.666788      0.619103           0.0           0.0   \n110                   0.152201      0.443324           0.0           0.0   \n111                   0.537548      0.719847           0.0           0.0   \n112                   0.737874      0.595790           0.0           0.0   \n\n     Carnivora  Cetacea  Chiroptera  Cingulata  Dasyuromorphia  \\\n0          1.0      0.0         0.0        0.0             0.0   \n1          1.0      0.0         0.0        0.0             0.0   \n2          0.0      0.0         0.0        0.0             0.0   \n3          0.0      NaN         0.0        0.0             0.0   \n4          0.0      0.0         0.0        0.0             0.0   \n..         ...      ...         ...        ...             ...   \n108        1.0      0.0         0.0        0.0             0.0   \n109        1.0      0.0         0.0        0.0             0.0   \n110        0.0      0.0         0.0        0.0             0.0   \n111        1.0      0.0         0.0        0.0             0.0   \n112        1.0      0.0         0.0        0.0             0.0   \n\n     Didelphimorphia  Diprotodontia  Erinaceomorpha  Lagomorpha  \\\n0                0.0            0.0             0.0         0.0   \n1                0.0            0.0             0.0         0.0   \n2                0.0            0.0             0.0         0.0   \n3                0.0            0.0             0.0         0.0   \n4                0.0            0.0             0.0         0.0   \n..               ...            ...             ...         ...   \n108              0.0            0.0             0.0         0.0   \n109              0.0            0.0             0.0         0.0   \n110              0.0            NaN             0.0         0.0   \n111              0.0            0.0             0.0         0.0   \n112              0.0            0.0             0.0         0.0   \n\n     Macroscelidea  Monotremata  Perissodactyla  Pholidota  Primates  \\\n0              0.0          0.0             0.0        0.0       0.0   \n1              0.0          0.0             0.0        0.0       0.0   \n2              0.0          0.0             0.0        0.0       1.0   \n3              0.0          0.0             0.0        0.0       0.0   \n4              0.0          0.0             0.0        0.0       0.0   \n..             ...          ...             ...        ...       ...   \n108            0.0          0.0             0.0        0.0       0.0   \n109            0.0          0.0             0.0        0.0       0.0   \n110            0.0          0.0             0.0        0.0       0.0   \n111            0.0          0.0             0.0        0.0       0.0   \n112            0.0          0.0             0.0        0.0       0.0   \n\n     Proboscidea  Rodentia  Sirenia  Soricomorpha  Tubulidentata     nchar  \\\n0            0.0       0.0      0.0           0.0            0.0  0.365854   \n1            0.0       0.0      0.0           0.0            0.0  0.365854   \n2            0.0       0.0      0.0           0.0            0.0  0.365854   \n3            0.0       0.0      0.0           0.0            0.0  0.341463   \n4            0.0       0.0      0.0           0.0            0.0  0.341463   \n..           ...       ...      ...           ...            ...       ...   \n108          0.0       0.0      0.0           0.0            0.0  0.365854   \n109          0.0       0.0      0.0           0.0            0.0  0.000000   \n110          0.0       0.0      0.0           0.0            0.0  0.463415   \n111          0.0       0.0      0.0           0.0            0.0  0.341463   \n112          0.0       0.0      0.0           0.0            0.0  0.390244   \n\n     haddock_score_sd  AA_83_Y  AA_30_negative  binary_haddock_score  \n0            0.160701      1.0             1.0                   1.0  \n1            0.212083      1.0             1.0                   0.0  \n2            0.482780      1.0             1.0                   1.0  \n3            0.290665      1.0             0.0                   0.0  \n4            0.548594      1.0             1.0                   0.0  \n..                ...      ...             ...                   ...  \n108          0.712780      1.0             1.0                   1.0  \n109          0.405247      1.0             1.0                   1.0  \n110          0.725709      0.0             1.0                   0.0  \n111          1.000000      1.0             1.0                   1.0  \n112          0.774647      1.0             1.0                   1.0  \n\n[113 rows x 76 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ForStrat_terrestrial</th>\n      <th>ForStrat_aquatic</th>\n      <th>Activity.Nocturnal</th>\n      <th>Activity.Crepuscular</th>\n      <th>Activity.Diurnal</th>\n      <th>female_maturity_d</th>\n      <th>male_maturity_d</th>\n      <th>weaning_d</th>\n      <th>litters_or_clutches_per_y</th>\n      <th>longevity_y</th>\n      <th>adult_svl_cm</th>\n      <th>metabolicRate_W</th>\n      <th>temperature_K</th>\n      <th>diet_breadth</th>\n      <th>development_d</th>\n      <th>mass_specific_production</th>\n      <th>log_litterclutch_size_n</th>\n      <th>log_inter_litterbirth_interval_y</th>\n      <th>log_birthhatching_weight_g</th>\n      <th>log_weaning_weight_g</th>\n      <th>log_adult_body_mass_g</th>\n      <th>log_female_body_mass_g</th>\n      <th>log_male_body_mass_g</th>\n      <th>tnc_ecoregion_breadth</th>\n      <th>log_range_size</th>\n      <th>X2.1_AgeatEyeOpening_d</th>\n      <th>X9.1_GestationLen_d</th>\n      <th>X10.2_SocialGrpSize</th>\n      <th>X24.1_TeatNumber</th>\n      <th>X6.2_TrophicLevel</th>\n      <th>X13.3_WeaningHeadBodyLen_mm</th>\n      <th>X26.2_GR_MaxLat_dd</th>\n      <th>X26.3_GR_MinLat_dd</th>\n      <th>X26.4_GR_MidRangeLat_dd</th>\n      <th>X26.5_GR_MaxLong_dd</th>\n      <th>X26.6_GR_MinLong_dd</th>\n      <th>X26.7_GR_MidRangeLong_dd</th>\n      <th>X27.4_HuPopDen_Change</th>\n      <th>X28.1_Precip_Mean_mm</th>\n      <th>X28.2_Temp_Mean_01degC</th>\n      <th>X30.1_AET_Mean_mm</th>\n      <th>X30.2_PET_Mean_mm</th>\n      <th>log_HomeRange_km2</th>\n      <th>log_HomeRange_Indiv_km2</th>\n      <th>log_PopulationDensity_n.km2</th>\n      <th>log_HuPopDen_Min_n.km2</th>\n      <th>log_HuPopDen_Mean_n.km2</th>\n      <th>log_HuPopDen_5p_n.km2</th>\n      <th>log_NeonateHeadBodyLen_mm</th>\n      <th>log_WOS_hits</th>\n      <th>Afrosoricida</th>\n      <th>Artiodactyla</th>\n      <th>Carnivora</th>\n      <th>Cetacea</th>\n      <th>Chiroptera</th>\n      <th>Cingulata</th>\n      <th>Dasyuromorphia</th>\n      <th>Didelphimorphia</th>\n      <th>Diprotodontia</th>\n      <th>Erinaceomorpha</th>\n      <th>Lagomorpha</th>\n      <th>Macroscelidea</th>\n      <th>Monotremata</th>\n      <th>Perissodactyla</th>\n      <th>Pholidota</th>\n      <th>Primates</th>\n      <th>Proboscidea</th>\n      <th>Rodentia</th>\n      <th>Sirenia</th>\n      <th>Soricomorpha</th>\n      <th>Tubulidentata</th>\n      <th>nchar</th>\n      <th>haddock_score_sd</th>\n      <th>AA_83_Y</th>\n      <th>AA_30_negative</th>\n      <th>binary_haddock_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.065797</td>\n      <td>0.075841</td>\n      <td>0.034741</td>\n      <td>0.210526</td>\n      <td>0.138029</td>\n      <td>0.067902</td>\n      <td>0.027137</td>\n      <td>0.864198</td>\n      <td>0.6</td>\n      <td>0.047435</td>\n      <td>0.064324</td>\n      <td>0.712741</td>\n      <td>0.625904</td>\n      <td>0.344187</td>\n      <td>0.255546</td>\n      <td>0.357657</td>\n      <td>0.497357</td>\n      <td>0.429557</td>\n      <td>NaN</td>\n      <td>0.741971</td>\n      <td>NaN</td>\n      <td>0.051922</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.901577</td>\n      <td>0.818372</td>\n      <td>0.941017</td>\n      <td>0.169165</td>\n      <td>0.036599</td>\n      <td>0.014533</td>\n      <td>0.200000</td>\n      <td>0.187966</td>\n      <td>0.194218</td>\n      <td>0.198967</td>\n      <td>0.104034</td>\n      <td>0.369099</td>\n      <td>0.349669</td>\n      <td>0.633553</td>\n      <td>0.514713</td>\n      <td>0.411510</td>\n      <td>0.162561</td>\n      <td>0.273348</td>\n      <td>0.655406</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365854</td>\n      <td>0.160701</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.144858</td>\n      <td>0.095436</td>\n      <td>0.104223</td>\n      <td>0.131579</td>\n      <td>0.269841</td>\n      <td>0.257353</td>\n      <td>0.627047</td>\n      <td>0.864198</td>\n      <td>0.0</td>\n      <td>0.164926</td>\n      <td>0.047067</td>\n      <td>0.510529</td>\n      <td>0.719685</td>\n      <td>0.573453</td>\n      <td>0.473731</td>\n      <td>0.635173</td>\n      <td>0.756037</td>\n      <td>0.687300</td>\n      <td>0.320513</td>\n      <td>0.647623</td>\n      <td>0.237632</td>\n      <td>0.170946</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>0.630613</td>\n      <td>0.250419</td>\n      <td>0.483326</td>\n      <td>0.579751</td>\n      <td>0.508595</td>\n      <td>0.528799</td>\n      <td>0.600000</td>\n      <td>0.170364</td>\n      <td>0.876430</td>\n      <td>0.288623</td>\n      <td>0.812691</td>\n      <td>0.870614</td>\n      <td>0.828728</td>\n      <td>0.024512</td>\n      <td>0.483852</td>\n      <td>0.409161</td>\n      <td>0.140070</td>\n      <td>0.565604</td>\n      <td>0.580571</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365854</td>\n      <td>0.212083</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.167050</td>\n      <td>0.134908</td>\n      <td>0.215298</td>\n      <td>0.308798</td>\n      <td>0.149074</td>\n      <td>0.057181</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0.250944</td>\n      <td>0.135447</td>\n      <td>0.018041</td>\n      <td>0.598573</td>\n      <td>0.466081</td>\n      <td>0.364180</td>\n      <td>0.348787</td>\n      <td>0.536702</td>\n      <td>0.376954</td>\n      <td>0.076923</td>\n      <td>0.436531</td>\n      <td>NaN</td>\n      <td>0.206166</td>\n      <td>0.046875</td>\n      <td>NaN</td>\n      <td>0.5</td>\n      <td>NaN</td>\n      <td>0.305167</td>\n      <td>0.488989</td>\n      <td>0.391309</td>\n      <td>0.130373</td>\n      <td>0.320548</td>\n      <td>0.169362</td>\n      <td>0.300000</td>\n      <td>0.927452</td>\n      <td>0.972512</td>\n      <td>0.902184</td>\n      <td>0.837602</td>\n      <td>0.364136</td>\n      <td>0.355636</td>\n      <td>0.581153</td>\n      <td>0.641462</td>\n      <td>0.000000</td>\n      <td>0.266244</td>\n      <td>0.314744</td>\n      <td>0.407636</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365854</td>\n      <td>0.482780</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.706728</td>\n      <td>0.621946</td>\n      <td>0.176509</td>\n      <td>0.210526</td>\n      <td>0.738095</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.2</td>\n      <td>0.654653</td>\n      <td>0.062315</td>\n      <td>0.009111</td>\n      <td>0.685771</td>\n      <td>0.949202</td>\n      <td>1.000000</td>\n      <td>0.979500</td>\n      <td>0.853961</td>\n      <td>0.949329</td>\n      <td>0.589744</td>\n      <td>0.943420</td>\n      <td>NaN</td>\n      <td>0.625145</td>\n      <td>0.015625</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>0.420887</td>\n      <td>0.562710</td>\n      <td>0.503766</td>\n      <td>0.491682</td>\n      <td>0.600141</td>\n      <td>0.537498</td>\n      <td>NaN</td>\n      <td>0.586530</td>\n      <td>0.709050</td>\n      <td>0.484833</td>\n      <td>0.672557</td>\n      <td>0.731489</td>\n      <td>0.369072</td>\n      <td>0.274731</td>\n      <td>0.633855</td>\n      <td>0.214465</td>\n      <td>0.314263</td>\n      <td>NaN</td>\n      <td>0.534133</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.341463</td>\n      <td>0.290665</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.117562</td>\n      <td>0.152370</td>\n      <td>0.084097</td>\n      <td>0.228638</td>\n      <td>0.146755</td>\n      <td>0.059387</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.233807</td>\n      <td>0.303096</td>\n      <td>0.246598</td>\n      <td>0.585111</td>\n      <td>0.388389</td>\n      <td>0.291012</td>\n      <td>NaN</td>\n      <td>0.336066</td>\n      <td>0.325862</td>\n      <td>0.038462</td>\n      <td>0.432175</td>\n      <td>NaN</td>\n      <td>0.206341</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.437272</td>\n      <td>0.548509</td>\n      <td>0.503766</td>\n      <td>0.491682</td>\n      <td>0.600141</td>\n      <td>0.537498</td>\n      <td>NaN</td>\n      <td>0.590206</td>\n      <td>0.720736</td>\n      <td>0.472741</td>\n      <td>0.686570</td>\n      <td>0.474696</td>\n      <td>0.347859</td>\n      <td>0.703429</td>\n      <td>0.678321</td>\n      <td>0.264731</td>\n      <td>0.330511</td>\n      <td>0.302667</td>\n      <td>0.317711</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.341463</td>\n      <td>0.548594</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.334200</td>\n      <td>0.292992</td>\n      <td>0.231208</td>\n      <td>0.052632</td>\n      <td>0.658730</td>\n      <td>0.399374</td>\n      <td>NaN</td>\n      <td>0.654321</td>\n      <td>0.6</td>\n      <td>0.195922</td>\n      <td>0.035384</td>\n      <td>0.378810</td>\n      <td>0.835007</td>\n      <td>0.577584</td>\n      <td>0.671043</td>\n      <td>0.742567</td>\n      <td>0.841876</td>\n      <td>0.768516</td>\n      <td>0.525641</td>\n      <td>0.824058</td>\n      <td>0.837368</td>\n      <td>0.460971</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.5</td>\n      <td>NaN</td>\n      <td>0.909545</td>\n      <td>0.862416</td>\n      <td>0.968112</td>\n      <td>1.000000</td>\n      <td>0.036599</td>\n      <td>0.463877</td>\n      <td>0.071655</td>\n      <td>0.100771</td>\n      <td>0.000000</td>\n      <td>0.090574</td>\n      <td>0.015616</td>\n      <td>0.956536</td>\n      <td>0.911610</td>\n      <td>0.073087</td>\n      <td>0.487184</td>\n      <td>0.367537</td>\n      <td>0.141950</td>\n      <td>0.665867</td>\n      <td>0.664675</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365854</td>\n      <td>0.712780</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.442722</td>\n      <td>0.390041</td>\n      <td>0.367106</td>\n      <td>0.052632</td>\n      <td>0.663095</td>\n      <td>0.392454</td>\n      <td>NaN</td>\n      <td>0.592593</td>\n      <td>0.2</td>\n      <td>0.203055</td>\n      <td>0.034904</td>\n      <td>0.237672</td>\n      <td>0.809091</td>\n      <td>0.592642</td>\n      <td>0.642024</td>\n      <td>0.772554</td>\n      <td>0.872286</td>\n      <td>0.830967</td>\n      <td>0.166667</td>\n      <td>0.781556</td>\n      <td>0.897895</td>\n      <td>0.111835</td>\n      <td>NaN</td>\n      <td>0.2</td>\n      <td>0.5</td>\n      <td>NaN</td>\n      <td>0.414198</td>\n      <td>0.555062</td>\n      <td>0.503766</td>\n      <td>0.491682</td>\n      <td>0.600141</td>\n      <td>0.537498</td>\n      <td>NaN</td>\n      <td>0.590849</td>\n      <td>0.722581</td>\n      <td>0.466388</td>\n      <td>0.673036</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.635830</td>\n      <td>0.225229</td>\n      <td>0.298615</td>\n      <td>0.666788</td>\n      <td>0.619103</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.405247</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.182541</td>\n      <td>0.158598</td>\n      <td>0.453702</td>\n      <td>0.150000</td>\n      <td>0.382540</td>\n      <td>0.186444</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.028600</td>\n      <td>0.034147</td>\n      <td>0.009111</td>\n      <td>0.790126</td>\n      <td>0.179724</td>\n      <td>0.555667</td>\n      <td>0.588676</td>\n      <td>0.767786</td>\n      <td>0.713814</td>\n      <td>0.089744</td>\n      <td>0.394868</td>\n      <td>NaN</td>\n      <td>0.037550</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.097940</td>\n      <td>0.099203</td>\n      <td>0.063464</td>\n      <td>0.902423</td>\n      <td>0.981855</td>\n      <td>0.996300</td>\n      <td>0.100000</td>\n      <td>0.227109</td>\n      <td>0.556746</td>\n      <td>0.298893</td>\n      <td>0.428575</td>\n      <td>0.392383</td>\n      <td>0.355906</td>\n      <td>0.575345</td>\n      <td>0.335211</td>\n      <td>0.448604</td>\n      <td>0.156351</td>\n      <td>0.152201</td>\n      <td>0.443324</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.463415</td>\n      <td>0.725709</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.067445</td>\n      <td>0.060396</td>\n      <td>0.035592</td>\n      <td>0.210526</td>\n      <td>0.206349</td>\n      <td>0.103641</td>\n      <td>0.138985</td>\n      <td>0.827160</td>\n      <td>0.4</td>\n      <td>0.082913</td>\n      <td>0.102708</td>\n      <td>0.710128</td>\n      <td>0.625904</td>\n      <td>0.485304</td>\n      <td>0.448767</td>\n      <td>0.469748</td>\n      <td>0.678059</td>\n      <td>0.523919</td>\n      <td>0.653846</td>\n      <td>0.886529</td>\n      <td>0.368684</td>\n      <td>0.085773</td>\n      <td>NaN</td>\n      <td>0.6</td>\n      <td>1.0</td>\n      <td>0.03891</td>\n      <td>0.922904</td>\n      <td>0.684354</td>\n      <td>0.888287</td>\n      <td>1.000000</td>\n      <td>0.000031</td>\n      <td>0.441257</td>\n      <td>0.250000</td>\n      <td>0.150321</td>\n      <td>0.255545</td>\n      <td>0.166015</td>\n      <td>0.189559</td>\n      <td>0.650299</td>\n      <td>0.612760</td>\n      <td>0.353916</td>\n      <td>0.395564</td>\n      <td>0.581328</td>\n      <td>0.138951</td>\n      <td>0.537548</td>\n      <td>0.719847</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.341463</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.419004</td>\n      <td>0.411249</td>\n      <td>0.353401</td>\n      <td>0.181579</td>\n      <td>0.444444</td>\n      <td>0.394814</td>\n      <td>NaN</td>\n      <td>0.753086</td>\n      <td>0.2</td>\n      <td>0.627112</td>\n      <td>0.061690</td>\n      <td>0.052093</td>\n      <td>0.790126</td>\n      <td>0.727346</td>\n      <td>0.668061</td>\n      <td>0.713890</td>\n      <td>0.916977</td>\n      <td>0.824376</td>\n      <td>0.038462</td>\n      <td>0.545340</td>\n      <td>0.000000</td>\n      <td>0.723370</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.5</td>\n      <td>NaN</td>\n      <td>0.438602</td>\n      <td>0.549152</td>\n      <td>0.503766</td>\n      <td>0.491682</td>\n      <td>0.600141</td>\n      <td>0.537498</td>\n      <td>NaN</td>\n      <td>0.590394</td>\n      <td>0.721987</td>\n      <td>0.456571</td>\n      <td>0.687951</td>\n      <td>0.721192</td>\n      <td>0.378023</td>\n      <td>0.298662</td>\n      <td>0.627276</td>\n      <td>0.226132</td>\n      <td>0.336578</td>\n      <td>0.737874</td>\n      <td>0.595790</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.390244</td>\n      <td>0.774647</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>113 rows × 76 columns</p>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalization (MinMaxScaler)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "data_scaled = scaler.fit_transform(data)\n",
    "data_scaled = pd.DataFrame(data_scaled, columns=data.columns)\n",
    "data = data_scaled\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:50.576618710Z",
     "start_time": "2023-10-05T18:31:50.338871310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     ForStrat_terrestrial  ForStrat_aquatic  Activity.Nocturnal  \\\n0                     1.0               0.0                 1.0   \n1                     1.0               0.0                 0.0   \n2                     1.0               0.0                 1.0   \n3                     0.0               1.0                 0.0   \n4                     1.0               0.0                 1.0   \n..                    ...               ...                 ...   \n108                   1.0               0.0                 0.0   \n109                   1.0               0.0                 0.0   \n110                   1.0               0.0                 1.0   \n111                   1.0               0.0                 1.0   \n112                   0.0               1.0                 1.0   \n\n     Activity.Crepuscular  Activity.Diurnal  female_maturity_d  \\\n0                     1.0               0.0           0.065797   \n1                     0.0               1.0           0.144858   \n2                     0.0               0.0           0.167050   \n3                     0.0               1.0           0.706728   \n4                     0.0               0.0           0.117562   \n..                    ...               ...                ...   \n108                   1.0               1.0           0.334200   \n109                   1.0               1.0           0.442722   \n110                   0.0               0.0           0.182541   \n111                   1.0               0.0           0.067445   \n112                   1.0               0.0           0.419004   \n\n     male_maturity_d  weaning_d  litters_or_clutches_per_y  longevity_y  \\\n0           0.075841   0.034741                   0.210526     0.138029   \n1           0.095436   0.104223                   0.131579     0.269841   \n2           0.134908   0.215298                   0.308798     0.149074   \n3           0.621946   0.176509                   0.210526     0.738095   \n4           0.152370   0.084097                   0.228638     0.146755   \n..               ...        ...                        ...          ...   \n108         0.292992   0.231208                   0.052632     0.658730   \n109         0.390041   0.367106                   0.052632     0.663095   \n110         0.158598   0.453702                   0.150000     0.382540   \n111         0.060396   0.035592                   0.210526     0.206349   \n112         0.411249   0.353401                   0.181579     0.444444   \n\n     adult_svl_cm  metabolicRate_W  temperature_K  diet_breadth  \\\n0        0.067902         0.027137       0.864198          0.60   \n1        0.257353         0.627047       0.864198          0.00   \n2        0.057181         0.019523       0.518519          1.00   \n3        0.650588         0.540504       0.587654          0.20   \n4        0.059387         0.024461       0.397531          0.04   \n..            ...              ...            ...           ...   \n108      0.399374         0.261247       0.654321          0.60   \n109      0.392454         0.456740       0.592593          0.20   \n110      0.186444         0.111785       0.360494          0.00   \n111      0.103641         0.138985       0.827160          0.40   \n112      0.394814         0.539606       0.753086          0.20   \n\n     development_d  mass_specific_production  log_litterclutch_size_n  \\\n0         0.047435                  0.064324                 0.712741   \n1         0.164926                  0.047067                 0.510529   \n2         0.250944                  0.135447                 0.018041   \n3         0.654653                  0.062315                 0.009111   \n4         0.233807                  0.303096                 0.246598   \n..             ...                       ...                      ...   \n108       0.195922                  0.035384                 0.378810   \n109       0.203055                  0.034904                 0.237672   \n110       0.028600                  0.034147                 0.009111   \n111       0.082913                  0.102708                 0.710128   \n112       0.627112                  0.061690                 0.052093   \n\n     log_inter_litterbirth_interval_y  log_birthhatching_weight_g  \\\n0                            0.625904                    0.344187   \n1                            0.719685                    0.573453   \n2                            0.598573                    0.466081   \n3                            0.685771                    0.949202   \n4                            0.585111                    0.388389   \n..                                ...                         ...   \n108                          0.835007                    0.577584   \n109                          0.809091                    0.592642   \n110                          0.790126                    0.179724   \n111                          0.625904                    0.485304   \n112                          0.790126                    0.727346   \n\n     log_weaning_weight_g  log_adult_body_mass_g  log_female_body_mass_g  \\\n0                0.255546               0.357657                0.497357   \n1                0.473731               0.635173                0.756037   \n2                0.364180               0.348787                0.536702   \n3                1.000000               0.979500                0.853961   \n4                0.291012               0.143948                0.336066   \n..                    ...                    ...                     ...   \n108              0.671043               0.742567                0.841876   \n109              0.642024               0.772554                0.872286   \n110              0.555667               0.588676                0.767786   \n111              0.448767               0.469748                0.678059   \n112              0.668061               0.713890                0.916977   \n\n     log_male_body_mass_g  tnc_ecoregion_breadth  log_range_size  \\\n0                0.429557               0.487179        0.741971   \n1                0.687300               0.320513        0.647623   \n2                0.376954               0.076923        0.436531   \n3                0.949329               0.589744        0.943420   \n4                0.325862               0.038462        0.432175   \n..                    ...                    ...             ...   \n108              0.768516               0.525641        0.824058   \n109              0.830967               0.166667        0.781556   \n110              0.713814               0.089744        0.394868   \n111              0.523919               0.653846        0.886529   \n112              0.824376               0.038462        0.545340   \n\n     X2.1_AgeatEyeOpening_d  X9.1_GestationLen_d  X10.2_SocialGrpSize  \\\n0                  0.571579             0.051922             0.009375   \n1                  0.237632             0.170946             0.245312   \n2                  0.315316             0.206166             0.046875   \n3                  0.047526             0.625145             0.015625   \n4                  0.165789             0.206341             0.026719   \n..                      ...                  ...                  ...   \n108                0.837368             0.460971             0.442969   \n109                0.897895             0.111835             0.271875   \n110                0.092263             0.037550             0.000000   \n111                0.368684             0.085773             0.003906   \n112                0.000000             0.723370             0.142813   \n\n     X24.1_TeatNumber  X6.2_TrophicLevel  X13.3_WeaningHeadBodyLen_mm  \\\n0                0.54                0.8                     0.294645   \n1                1.00                1.0                     0.294645   \n2                0.20                0.5                     0.294645   \n3                0.24                1.0                     0.294645   \n4                0.22                0.6                     0.294645   \n..                ...                ...                          ...   \n108              0.56                0.5                     0.294645   \n109              0.20                0.5                     0.294645   \n110              0.16                0.0                     0.294645   \n111              0.60                1.0                     0.038910   \n112              0.12                0.5                     0.294645   \n\n     X26.2_GR_MaxLat_dd  X26.3_GR_MinLat_dd  X26.4_GR_MidRangeLat_dd  \\\n0              0.901577            0.818372                 0.941017   \n1              0.630613            0.250419                 0.483326   \n2              0.305167            0.488989                 0.391309   \n3              0.420887            0.562710                 0.503766   \n4              0.437272            0.548509                 0.503766   \n..                  ...                 ...                      ...   \n108            0.909545            0.862416                 0.968112   \n109            0.414198            0.555062                 0.503766   \n110            0.097940            0.099203                 0.063464   \n111            0.922904            0.684354                 0.888287   \n112            0.438602            0.549152                 0.503766   \n\n     X26.5_GR_MaxLong_dd  X26.6_GR_MinLong_dd  X26.7_GR_MidRangeLong_dd  \\\n0               0.169165             0.036599                  0.014533   \n1               0.579751             0.508595                  0.528799   \n2               0.130373             0.320548                  0.169362   \n3               0.491682             0.600141                  0.537498   \n4               0.491682             0.600141                  0.537498   \n..                   ...                  ...                       ...   \n108             1.000000             0.036599                  0.463877   \n109             0.491682             0.600141                  0.537498   \n110             0.902423             0.981855                  0.996300   \n111             1.000000             0.000031                  0.441257   \n112             0.491682             0.600141                  0.537498   \n\n     X27.4_HuPopDen_Change  X28.1_Precip_Mean_mm  X28.2_Temp_Mean_01degC  \\\n0                 0.200000              0.187966                0.194218   \n1                 0.600000              0.170364                0.876430   \n2                 0.300000              0.927452                0.972512   \n3                 0.570000              0.586530                0.709050   \n4                 0.353527              0.590206                0.720736   \n..                     ...                   ...                     ...   \n108               0.071655              0.100771                0.000000   \n109               0.474331              0.590849                0.722581   \n110               0.100000              0.227109                0.556746   \n111               0.250000              0.150321                0.255545   \n112               0.436880              0.590394                0.721987   \n\n     X30.1_AET_Mean_mm  X30.2_PET_Mean_mm  log_HomeRange_km2  \\\n0             0.198967           0.104034           0.369099   \n1             0.288623           0.812691           0.870614   \n2             0.902184           0.837602           0.364136   \n3             0.484833           0.672557           0.731489   \n4             0.472741           0.686570           0.474696   \n..                 ...                ...                ...   \n108           0.090574           0.015616           0.956536   \n109           0.466388           0.673036           0.848956   \n110           0.298893           0.428575           0.392383   \n111           0.166015           0.189559           0.650299   \n112           0.456571           0.687951           0.721192   \n\n     log_HomeRange_Indiv_km2  log_PopulationDensity_n.km2  \\\n0                   0.349669                     0.633553   \n1                   0.828728                     0.024512   \n2                   0.355636                     0.581153   \n3                   0.369072                     0.274731   \n4                   0.347859                     0.703429   \n..                       ...                          ...   \n108                 0.911610                     0.073087   \n109                 0.817994                     0.000000   \n110                 0.355906                     0.575345   \n111                 0.612760                     0.353916   \n112                 0.378023                     0.298662   \n\n     log_HuPopDen_Min_n.km2  log_HuPopDen_Mean_n.km2  log_HuPopDen_5p_n.km2  \\\n0                  0.514713                 0.411510               0.162561   \n1                  0.483852                 0.409161               0.140070   \n2                  0.641462                 0.000000               0.266244   \n3                  0.633855                 0.214465               0.314263   \n4                  0.678321                 0.264731               0.330511   \n..                      ...                      ...                    ...   \n108                0.487184                 0.367537               0.141950   \n109                0.635830                 0.225229               0.298615   \n110                0.335211                 0.448604               0.156351   \n111                0.395564                 0.581328               0.138951   \n112                0.627276                 0.226132               0.336578   \n\n     log_NeonateHeadBodyLen_mm  log_WOS_hits  Afrosoricida  Artiodactyla  \\\n0                     0.273348      0.655406           0.0           0.0   \n1                     0.565604      0.580571           0.0           0.0   \n2                     0.314744      0.407636           0.0           0.0   \n3                     0.866916      0.534133           0.0           0.0   \n4                     0.302667      0.317711           0.0           0.0   \n..                         ...           ...           ...           ...   \n108                   0.665867      0.664675           0.0           0.0   \n109                   0.666788      0.619103           0.0           0.0   \n110                   0.152201      0.443324           0.0           0.0   \n111                   0.537548      0.719847           0.0           0.0   \n112                   0.737874      0.595790           0.0           0.0   \n\n     Carnivora  Cetacea  Chiroptera  Cingulata  Dasyuromorphia  \\\n0          1.0      0.0         0.0        0.0             0.0   \n1          1.0      0.0         0.0        0.0             0.0   \n2          0.0      0.0         0.0        0.0             0.0   \n3          0.0      0.0         0.0        0.0             0.0   \n4          0.0      0.0         0.0        0.0             0.0   \n..         ...      ...         ...        ...             ...   \n108        1.0      0.0         0.0        0.0             0.0   \n109        1.0      0.0         0.0        0.0             0.0   \n110        0.0      0.0         0.0        0.0             0.0   \n111        1.0      0.0         0.0        0.0             0.0   \n112        1.0      0.0         0.0        0.0             0.0   \n\n     Didelphimorphia  Diprotodontia  Erinaceomorpha  Lagomorpha  \\\n0                0.0            0.0             0.0         0.0   \n1                0.0            0.0             0.0         0.0   \n2                0.0            0.0             0.0         0.0   \n3                0.0            0.0             0.0         0.0   \n4                0.0            0.0             0.0         0.0   \n..               ...            ...             ...         ...   \n108              0.0            0.0             0.0         0.0   \n109              0.0            0.0             0.0         0.0   \n110              0.0            0.0             0.0         0.0   \n111              0.0            0.0             0.0         0.0   \n112              0.0            0.0             0.0         0.0   \n\n     Macroscelidea  Monotremata  Perissodactyla  Pholidota  Primates  \\\n0              0.0          0.0             0.0        0.0       0.0   \n1              0.0          0.0             0.0        0.0       0.0   \n2              0.0          0.0             0.0        0.0       1.0   \n3              0.0          0.0             0.0        0.0       0.0   \n4              0.0          0.0             0.0        0.0       0.0   \n..             ...          ...             ...        ...       ...   \n108            0.0          0.0             0.0        0.0       0.0   \n109            0.0          0.0             0.0        0.0       0.0   \n110            0.0          0.0             0.0        0.0       0.0   \n111            0.0          0.0             0.0        0.0       0.0   \n112            0.0          0.0             0.0        0.0       0.0   \n\n     Proboscidea  Rodentia  Sirenia  Soricomorpha  Tubulidentata     nchar  \\\n0            0.0       0.0      0.0           0.0            0.0  0.365854   \n1            0.0       0.0      0.0           0.0            0.0  0.365854   \n2            0.0       0.0      0.0           0.0            0.0  0.365854   \n3            0.0       0.0      0.0           0.0            0.0  0.341463   \n4            0.0       0.0      0.0           0.0            0.0  0.341463   \n..           ...       ...      ...           ...            ...       ...   \n108          0.0       0.0      0.0           0.0            0.0  0.365854   \n109          0.0       0.0      0.0           0.0            0.0  0.000000   \n110          0.0       0.0      0.0           0.0            0.0  0.463415   \n111          0.0       0.0      0.0           0.0            0.0  0.341463   \n112          0.0       0.0      0.0           0.0            0.0  0.390244   \n\n     haddock_score_sd  AA_83_Y  AA_30_negative  binary_haddock_score  \n0            0.160701      1.0             1.0                   1.0  \n1            0.212083      1.0             1.0                   0.0  \n2            0.482780      1.0             1.0                   1.0  \n3            0.290665      1.0             0.0                   0.0  \n4            0.548594      1.0             1.0                   0.0  \n..                ...      ...             ...                   ...  \n108          0.712780      1.0             1.0                   1.0  \n109          0.405247      1.0             1.0                   1.0  \n110          0.725709      0.0             1.0                   0.0  \n111          1.000000      1.0             1.0                   1.0  \n112          0.774647      1.0             1.0                   1.0  \n\n[113 rows x 76 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ForStrat_terrestrial</th>\n      <th>ForStrat_aquatic</th>\n      <th>Activity.Nocturnal</th>\n      <th>Activity.Crepuscular</th>\n      <th>Activity.Diurnal</th>\n      <th>female_maturity_d</th>\n      <th>male_maturity_d</th>\n      <th>weaning_d</th>\n      <th>litters_or_clutches_per_y</th>\n      <th>longevity_y</th>\n      <th>adult_svl_cm</th>\n      <th>metabolicRate_W</th>\n      <th>temperature_K</th>\n      <th>diet_breadth</th>\n      <th>development_d</th>\n      <th>mass_specific_production</th>\n      <th>log_litterclutch_size_n</th>\n      <th>log_inter_litterbirth_interval_y</th>\n      <th>log_birthhatching_weight_g</th>\n      <th>log_weaning_weight_g</th>\n      <th>log_adult_body_mass_g</th>\n      <th>log_female_body_mass_g</th>\n      <th>log_male_body_mass_g</th>\n      <th>tnc_ecoregion_breadth</th>\n      <th>log_range_size</th>\n      <th>X2.1_AgeatEyeOpening_d</th>\n      <th>X9.1_GestationLen_d</th>\n      <th>X10.2_SocialGrpSize</th>\n      <th>X24.1_TeatNumber</th>\n      <th>X6.2_TrophicLevel</th>\n      <th>X13.3_WeaningHeadBodyLen_mm</th>\n      <th>X26.2_GR_MaxLat_dd</th>\n      <th>X26.3_GR_MinLat_dd</th>\n      <th>X26.4_GR_MidRangeLat_dd</th>\n      <th>X26.5_GR_MaxLong_dd</th>\n      <th>X26.6_GR_MinLong_dd</th>\n      <th>X26.7_GR_MidRangeLong_dd</th>\n      <th>X27.4_HuPopDen_Change</th>\n      <th>X28.1_Precip_Mean_mm</th>\n      <th>X28.2_Temp_Mean_01degC</th>\n      <th>X30.1_AET_Mean_mm</th>\n      <th>X30.2_PET_Mean_mm</th>\n      <th>log_HomeRange_km2</th>\n      <th>log_HomeRange_Indiv_km2</th>\n      <th>log_PopulationDensity_n.km2</th>\n      <th>log_HuPopDen_Min_n.km2</th>\n      <th>log_HuPopDen_Mean_n.km2</th>\n      <th>log_HuPopDen_5p_n.km2</th>\n      <th>log_NeonateHeadBodyLen_mm</th>\n      <th>log_WOS_hits</th>\n      <th>Afrosoricida</th>\n      <th>Artiodactyla</th>\n      <th>Carnivora</th>\n      <th>Cetacea</th>\n      <th>Chiroptera</th>\n      <th>Cingulata</th>\n      <th>Dasyuromorphia</th>\n      <th>Didelphimorphia</th>\n      <th>Diprotodontia</th>\n      <th>Erinaceomorpha</th>\n      <th>Lagomorpha</th>\n      <th>Macroscelidea</th>\n      <th>Monotremata</th>\n      <th>Perissodactyla</th>\n      <th>Pholidota</th>\n      <th>Primates</th>\n      <th>Proboscidea</th>\n      <th>Rodentia</th>\n      <th>Sirenia</th>\n      <th>Soricomorpha</th>\n      <th>Tubulidentata</th>\n      <th>nchar</th>\n      <th>haddock_score_sd</th>\n      <th>AA_83_Y</th>\n      <th>AA_30_negative</th>\n      <th>binary_haddock_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.065797</td>\n      <td>0.075841</td>\n      <td>0.034741</td>\n      <td>0.210526</td>\n      <td>0.138029</td>\n      <td>0.067902</td>\n      <td>0.027137</td>\n      <td>0.864198</td>\n      <td>0.60</td>\n      <td>0.047435</td>\n      <td>0.064324</td>\n      <td>0.712741</td>\n      <td>0.625904</td>\n      <td>0.344187</td>\n      <td>0.255546</td>\n      <td>0.357657</td>\n      <td>0.497357</td>\n      <td>0.429557</td>\n      <td>0.487179</td>\n      <td>0.741971</td>\n      <td>0.571579</td>\n      <td>0.051922</td>\n      <td>0.009375</td>\n      <td>0.54</td>\n      <td>0.8</td>\n      <td>0.294645</td>\n      <td>0.901577</td>\n      <td>0.818372</td>\n      <td>0.941017</td>\n      <td>0.169165</td>\n      <td>0.036599</td>\n      <td>0.014533</td>\n      <td>0.200000</td>\n      <td>0.187966</td>\n      <td>0.194218</td>\n      <td>0.198967</td>\n      <td>0.104034</td>\n      <td>0.369099</td>\n      <td>0.349669</td>\n      <td>0.633553</td>\n      <td>0.514713</td>\n      <td>0.411510</td>\n      <td>0.162561</td>\n      <td>0.273348</td>\n      <td>0.655406</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365854</td>\n      <td>0.160701</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.144858</td>\n      <td>0.095436</td>\n      <td>0.104223</td>\n      <td>0.131579</td>\n      <td>0.269841</td>\n      <td>0.257353</td>\n      <td>0.627047</td>\n      <td>0.864198</td>\n      <td>0.00</td>\n      <td>0.164926</td>\n      <td>0.047067</td>\n      <td>0.510529</td>\n      <td>0.719685</td>\n      <td>0.573453</td>\n      <td>0.473731</td>\n      <td>0.635173</td>\n      <td>0.756037</td>\n      <td>0.687300</td>\n      <td>0.320513</td>\n      <td>0.647623</td>\n      <td>0.237632</td>\n      <td>0.170946</td>\n      <td>0.245312</td>\n      <td>1.00</td>\n      <td>1.0</td>\n      <td>0.294645</td>\n      <td>0.630613</td>\n      <td>0.250419</td>\n      <td>0.483326</td>\n      <td>0.579751</td>\n      <td>0.508595</td>\n      <td>0.528799</td>\n      <td>0.600000</td>\n      <td>0.170364</td>\n      <td>0.876430</td>\n      <td>0.288623</td>\n      <td>0.812691</td>\n      <td>0.870614</td>\n      <td>0.828728</td>\n      <td>0.024512</td>\n      <td>0.483852</td>\n      <td>0.409161</td>\n      <td>0.140070</td>\n      <td>0.565604</td>\n      <td>0.580571</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365854</td>\n      <td>0.212083</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.167050</td>\n      <td>0.134908</td>\n      <td>0.215298</td>\n      <td>0.308798</td>\n      <td>0.149074</td>\n      <td>0.057181</td>\n      <td>0.019523</td>\n      <td>0.518519</td>\n      <td>1.00</td>\n      <td>0.250944</td>\n      <td>0.135447</td>\n      <td>0.018041</td>\n      <td>0.598573</td>\n      <td>0.466081</td>\n      <td>0.364180</td>\n      <td>0.348787</td>\n      <td>0.536702</td>\n      <td>0.376954</td>\n      <td>0.076923</td>\n      <td>0.436531</td>\n      <td>0.315316</td>\n      <td>0.206166</td>\n      <td>0.046875</td>\n      <td>0.20</td>\n      <td>0.5</td>\n      <td>0.294645</td>\n      <td>0.305167</td>\n      <td>0.488989</td>\n      <td>0.391309</td>\n      <td>0.130373</td>\n      <td>0.320548</td>\n      <td>0.169362</td>\n      <td>0.300000</td>\n      <td>0.927452</td>\n      <td>0.972512</td>\n      <td>0.902184</td>\n      <td>0.837602</td>\n      <td>0.364136</td>\n      <td>0.355636</td>\n      <td>0.581153</td>\n      <td>0.641462</td>\n      <td>0.000000</td>\n      <td>0.266244</td>\n      <td>0.314744</td>\n      <td>0.407636</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365854</td>\n      <td>0.482780</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.706728</td>\n      <td>0.621946</td>\n      <td>0.176509</td>\n      <td>0.210526</td>\n      <td>0.738095</td>\n      <td>0.650588</td>\n      <td>0.540504</td>\n      <td>0.587654</td>\n      <td>0.20</td>\n      <td>0.654653</td>\n      <td>0.062315</td>\n      <td>0.009111</td>\n      <td>0.685771</td>\n      <td>0.949202</td>\n      <td>1.000000</td>\n      <td>0.979500</td>\n      <td>0.853961</td>\n      <td>0.949329</td>\n      <td>0.589744</td>\n      <td>0.943420</td>\n      <td>0.047526</td>\n      <td>0.625145</td>\n      <td>0.015625</td>\n      <td>0.24</td>\n      <td>1.0</td>\n      <td>0.294645</td>\n      <td>0.420887</td>\n      <td>0.562710</td>\n      <td>0.503766</td>\n      <td>0.491682</td>\n      <td>0.600141</td>\n      <td>0.537498</td>\n      <td>0.570000</td>\n      <td>0.586530</td>\n      <td>0.709050</td>\n      <td>0.484833</td>\n      <td>0.672557</td>\n      <td>0.731489</td>\n      <td>0.369072</td>\n      <td>0.274731</td>\n      <td>0.633855</td>\n      <td>0.214465</td>\n      <td>0.314263</td>\n      <td>0.866916</td>\n      <td>0.534133</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.341463</td>\n      <td>0.290665</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.117562</td>\n      <td>0.152370</td>\n      <td>0.084097</td>\n      <td>0.228638</td>\n      <td>0.146755</td>\n      <td>0.059387</td>\n      <td>0.024461</td>\n      <td>0.397531</td>\n      <td>0.04</td>\n      <td>0.233807</td>\n      <td>0.303096</td>\n      <td>0.246598</td>\n      <td>0.585111</td>\n      <td>0.388389</td>\n      <td>0.291012</td>\n      <td>0.143948</td>\n      <td>0.336066</td>\n      <td>0.325862</td>\n      <td>0.038462</td>\n      <td>0.432175</td>\n      <td>0.165789</td>\n      <td>0.206341</td>\n      <td>0.026719</td>\n      <td>0.22</td>\n      <td>0.6</td>\n      <td>0.294645</td>\n      <td>0.437272</td>\n      <td>0.548509</td>\n      <td>0.503766</td>\n      <td>0.491682</td>\n      <td>0.600141</td>\n      <td>0.537498</td>\n      <td>0.353527</td>\n      <td>0.590206</td>\n      <td>0.720736</td>\n      <td>0.472741</td>\n      <td>0.686570</td>\n      <td>0.474696</td>\n      <td>0.347859</td>\n      <td>0.703429</td>\n      <td>0.678321</td>\n      <td>0.264731</td>\n      <td>0.330511</td>\n      <td>0.302667</td>\n      <td>0.317711</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.341463</td>\n      <td>0.548594</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.334200</td>\n      <td>0.292992</td>\n      <td>0.231208</td>\n      <td>0.052632</td>\n      <td>0.658730</td>\n      <td>0.399374</td>\n      <td>0.261247</td>\n      <td>0.654321</td>\n      <td>0.60</td>\n      <td>0.195922</td>\n      <td>0.035384</td>\n      <td>0.378810</td>\n      <td>0.835007</td>\n      <td>0.577584</td>\n      <td>0.671043</td>\n      <td>0.742567</td>\n      <td>0.841876</td>\n      <td>0.768516</td>\n      <td>0.525641</td>\n      <td>0.824058</td>\n      <td>0.837368</td>\n      <td>0.460971</td>\n      <td>0.442969</td>\n      <td>0.56</td>\n      <td>0.5</td>\n      <td>0.294645</td>\n      <td>0.909545</td>\n      <td>0.862416</td>\n      <td>0.968112</td>\n      <td>1.000000</td>\n      <td>0.036599</td>\n      <td>0.463877</td>\n      <td>0.071655</td>\n      <td>0.100771</td>\n      <td>0.000000</td>\n      <td>0.090574</td>\n      <td>0.015616</td>\n      <td>0.956536</td>\n      <td>0.911610</td>\n      <td>0.073087</td>\n      <td>0.487184</td>\n      <td>0.367537</td>\n      <td>0.141950</td>\n      <td>0.665867</td>\n      <td>0.664675</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365854</td>\n      <td>0.712780</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.442722</td>\n      <td>0.390041</td>\n      <td>0.367106</td>\n      <td>0.052632</td>\n      <td>0.663095</td>\n      <td>0.392454</td>\n      <td>0.456740</td>\n      <td>0.592593</td>\n      <td>0.20</td>\n      <td>0.203055</td>\n      <td>0.034904</td>\n      <td>0.237672</td>\n      <td>0.809091</td>\n      <td>0.592642</td>\n      <td>0.642024</td>\n      <td>0.772554</td>\n      <td>0.872286</td>\n      <td>0.830967</td>\n      <td>0.166667</td>\n      <td>0.781556</td>\n      <td>0.897895</td>\n      <td>0.111835</td>\n      <td>0.271875</td>\n      <td>0.20</td>\n      <td>0.5</td>\n      <td>0.294645</td>\n      <td>0.414198</td>\n      <td>0.555062</td>\n      <td>0.503766</td>\n      <td>0.491682</td>\n      <td>0.600141</td>\n      <td>0.537498</td>\n      <td>0.474331</td>\n      <td>0.590849</td>\n      <td>0.722581</td>\n      <td>0.466388</td>\n      <td>0.673036</td>\n      <td>0.848956</td>\n      <td>0.817994</td>\n      <td>0.000000</td>\n      <td>0.635830</td>\n      <td>0.225229</td>\n      <td>0.298615</td>\n      <td>0.666788</td>\n      <td>0.619103</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.405247</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.182541</td>\n      <td>0.158598</td>\n      <td>0.453702</td>\n      <td>0.150000</td>\n      <td>0.382540</td>\n      <td>0.186444</td>\n      <td>0.111785</td>\n      <td>0.360494</td>\n      <td>0.00</td>\n      <td>0.028600</td>\n      <td>0.034147</td>\n      <td>0.009111</td>\n      <td>0.790126</td>\n      <td>0.179724</td>\n      <td>0.555667</td>\n      <td>0.588676</td>\n      <td>0.767786</td>\n      <td>0.713814</td>\n      <td>0.089744</td>\n      <td>0.394868</td>\n      <td>0.092263</td>\n      <td>0.037550</td>\n      <td>0.000000</td>\n      <td>0.16</td>\n      <td>0.0</td>\n      <td>0.294645</td>\n      <td>0.097940</td>\n      <td>0.099203</td>\n      <td>0.063464</td>\n      <td>0.902423</td>\n      <td>0.981855</td>\n      <td>0.996300</td>\n      <td>0.100000</td>\n      <td>0.227109</td>\n      <td>0.556746</td>\n      <td>0.298893</td>\n      <td>0.428575</td>\n      <td>0.392383</td>\n      <td>0.355906</td>\n      <td>0.575345</td>\n      <td>0.335211</td>\n      <td>0.448604</td>\n      <td>0.156351</td>\n      <td>0.152201</td>\n      <td>0.443324</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.463415</td>\n      <td>0.725709</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.067445</td>\n      <td>0.060396</td>\n      <td>0.035592</td>\n      <td>0.210526</td>\n      <td>0.206349</td>\n      <td>0.103641</td>\n      <td>0.138985</td>\n      <td>0.827160</td>\n      <td>0.40</td>\n      <td>0.082913</td>\n      <td>0.102708</td>\n      <td>0.710128</td>\n      <td>0.625904</td>\n      <td>0.485304</td>\n      <td>0.448767</td>\n      <td>0.469748</td>\n      <td>0.678059</td>\n      <td>0.523919</td>\n      <td>0.653846</td>\n      <td>0.886529</td>\n      <td>0.368684</td>\n      <td>0.085773</td>\n      <td>0.003906</td>\n      <td>0.60</td>\n      <td>1.0</td>\n      <td>0.038910</td>\n      <td>0.922904</td>\n      <td>0.684354</td>\n      <td>0.888287</td>\n      <td>1.000000</td>\n      <td>0.000031</td>\n      <td>0.441257</td>\n      <td>0.250000</td>\n      <td>0.150321</td>\n      <td>0.255545</td>\n      <td>0.166015</td>\n      <td>0.189559</td>\n      <td>0.650299</td>\n      <td>0.612760</td>\n      <td>0.353916</td>\n      <td>0.395564</td>\n      <td>0.581328</td>\n      <td>0.138951</td>\n      <td>0.537548</td>\n      <td>0.719847</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.341463</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.419004</td>\n      <td>0.411249</td>\n      <td>0.353401</td>\n      <td>0.181579</td>\n      <td>0.444444</td>\n      <td>0.394814</td>\n      <td>0.539606</td>\n      <td>0.753086</td>\n      <td>0.20</td>\n      <td>0.627112</td>\n      <td>0.061690</td>\n      <td>0.052093</td>\n      <td>0.790126</td>\n      <td>0.727346</td>\n      <td>0.668061</td>\n      <td>0.713890</td>\n      <td>0.916977</td>\n      <td>0.824376</td>\n      <td>0.038462</td>\n      <td>0.545340</td>\n      <td>0.000000</td>\n      <td>0.723370</td>\n      <td>0.142813</td>\n      <td>0.12</td>\n      <td>0.5</td>\n      <td>0.294645</td>\n      <td>0.438602</td>\n      <td>0.549152</td>\n      <td>0.503766</td>\n      <td>0.491682</td>\n      <td>0.600141</td>\n      <td>0.537498</td>\n      <td>0.436880</td>\n      <td>0.590394</td>\n      <td>0.721987</td>\n      <td>0.456571</td>\n      <td>0.687951</td>\n      <td>0.721192</td>\n      <td>0.378023</td>\n      <td>0.298662</td>\n      <td>0.627276</td>\n      <td>0.226132</td>\n      <td>0.336578</td>\n      <td>0.737874</td>\n      <td>0.595790</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.390244</td>\n      <td>0.774647</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>113 rows × 76 columns</p>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill missing values with k-NN\n",
    "from sklearn.impute import KNNImputer\n",
    "imputer = KNNImputer(n_neighbors=5)\n",
    "data = pd.DataFrame(imputer.fit_transform(data),columns = data.columns)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:50.695298459Z",
     "start_time": "2023-10-05T18:31:50.499948592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     ForStrat_terrestrial  ForStrat_aquatic  Activity.Nocturnal  \\\n0                     1.0               0.0                 1.0   \n1                     1.0               0.0                 0.0   \n2                     1.0               0.0                 1.0   \n3                     0.0               1.0                 0.0   \n4                     1.0               0.0                 1.0   \n..                    ...               ...                 ...   \n108                   1.0               0.0                 0.0   \n109                   1.0               0.0                 0.0   \n110                   1.0               0.0                 1.0   \n111                   1.0               0.0                 1.0   \n112                   0.0               1.0                 1.0   \n\n     Activity.Crepuscular  Activity.Diurnal  female_maturity_d  \\\n0                     1.0               0.0           0.065797   \n1                     0.0               1.0           0.144858   \n2                     0.0               0.0           0.167050   \n3                     0.0               1.0           0.706728   \n4                     0.0               0.0           0.117562   \n..                    ...               ...                ...   \n108                   1.0               1.0           0.334200   \n109                   1.0               1.0           0.442722   \n110                   0.0               0.0           0.182541   \n111                   1.0               0.0           0.067445   \n112                   1.0               0.0           0.419004   \n\n     male_maturity_d  weaning_d  litters_or_clutches_per_y  longevity_y  \\\n0           0.075841   0.034741                   0.210526     0.138029   \n1           0.095436   0.104223                   0.131579     0.269841   \n2           0.134908   0.215298                   0.308798     0.149074   \n3           0.621946   0.176509                   0.210526     0.738095   \n4           0.152370   0.084097                   0.228638     0.146755   \n..               ...        ...                        ...          ...   \n108         0.292992   0.231208                   0.052632     0.658730   \n109         0.390041   0.367106                   0.052632     0.663095   \n110         0.158598   0.453702                   0.150000     0.382540   \n111         0.060396   0.035592                   0.210526     0.206349   \n112         0.411249   0.353401                   0.181579     0.444444   \n\n     adult_svl_cm  metabolicRate_W  temperature_K  diet_breadth  \\\n0        0.067902         0.027137       0.864198          0.60   \n1        0.257353         0.627047       0.864198          0.00   \n2        0.057181         0.019523       0.518519          1.00   \n3        0.650588         0.540504       0.587654          0.20   \n4        0.059387         0.024461       0.397531          0.04   \n..            ...              ...            ...           ...   \n108      0.399374         0.261247       0.654321          0.60   \n109      0.392454         0.456740       0.592593          0.20   \n110      0.186444         0.111785       0.360494          0.00   \n111      0.103641         0.138985       0.827160          0.40   \n112      0.394814         0.539606       0.753086          0.20   \n\n     development_d  mass_specific_production  log_litterclutch_size_n  \\\n0         0.047435                  0.064324                 0.712741   \n1         0.164926                  0.047067                 0.510529   \n2         0.250944                  0.135447                 0.018041   \n3         0.654653                  0.062315                 0.009111   \n4         0.233807                  0.303096                 0.246598   \n..             ...                       ...                      ...   \n108       0.195922                  0.035384                 0.378810   \n109       0.203055                  0.034904                 0.237672   \n110       0.028600                  0.034147                 0.009111   \n111       0.082913                  0.102708                 0.710128   \n112       0.627112                  0.061690                 0.052093   \n\n     log_inter_litterbirth_interval_y  log_birthhatching_weight_g  \\\n0                            0.625904                    0.344187   \n1                            0.719685                    0.573453   \n2                            0.598573                    0.466081   \n3                            0.685771                    0.949202   \n4                            0.585111                    0.388389   \n..                                ...                         ...   \n108                          0.835007                    0.577584   \n109                          0.809091                    0.592642   \n110                          0.790126                    0.179724   \n111                          0.625904                    0.485304   \n112                          0.790126                    0.727346   \n\n     log_weaning_weight_g  log_adult_body_mass_g  log_female_body_mass_g  \\\n0                0.255546               0.357657                0.497357   \n1                0.473731               0.635173                0.756037   \n2                0.364180               0.348787                0.536702   \n3                1.000000               0.979500                0.853961   \n4                0.291012               0.143948                0.336066   \n..                    ...                    ...                     ...   \n108              0.671043               0.742567                0.841876   \n109              0.642024               0.772554                0.872286   \n110              0.555667               0.588676                0.767786   \n111              0.448767               0.469748                0.678059   \n112              0.668061               0.713890                0.916977   \n\n     log_male_body_mass_g  tnc_ecoregion_breadth  log_range_size  \\\n0                0.429557               0.487179        0.741971   \n1                0.687300               0.320513        0.647623   \n2                0.376954               0.076923        0.436531   \n3                0.949329               0.589744        0.943420   \n4                0.325862               0.038462        0.432175   \n..                    ...                    ...             ...   \n108              0.768516               0.525641        0.824058   \n109              0.830967               0.166667        0.781556   \n110              0.713814               0.089744        0.394868   \n111              0.523919               0.653846        0.886529   \n112              0.824376               0.038462        0.545340   \n\n     X2.1_AgeatEyeOpening_d  X9.1_GestationLen_d  X10.2_SocialGrpSize  \\\n0                  0.571579             0.051922             0.009375   \n1                  0.237632             0.170946             0.245312   \n2                  0.315316             0.206166             0.046875   \n3                  0.047526             0.625145             0.015625   \n4                  0.165789             0.206341             0.026719   \n..                      ...                  ...                  ...   \n108                0.837368             0.460971             0.442969   \n109                0.897895             0.111835             0.271875   \n110                0.092263             0.037550             0.000000   \n111                0.368684             0.085773             0.003906   \n112                0.000000             0.723370             0.142813   \n\n     X24.1_TeatNumber  X6.2_TrophicLevel  X13.3_WeaningHeadBodyLen_mm  \\\n0                0.54                0.8                     0.294645   \n1                1.00                1.0                     0.294645   \n2                0.20                0.5                     0.294645   \n3                0.24                1.0                     0.294645   \n4                0.22                0.6                     0.294645   \n..                ...                ...                          ...   \n108              0.56                0.5                     0.294645   \n109              0.20                0.5                     0.294645   \n110              0.16                0.0                     0.294645   \n111              0.60                1.0                     0.038910   \n112              0.12                0.5                     0.294645   \n\n     X26.2_GR_MaxLat_dd  X26.3_GR_MinLat_dd  X26.4_GR_MidRangeLat_dd  \\\n0              0.901577            0.818372                 0.941017   \n1              0.630613            0.250419                 0.483326   \n2              0.305167            0.488989                 0.391309   \n3              0.420887            0.562710                 0.503766   \n4              0.437272            0.548509                 0.503766   \n..                  ...                 ...                      ...   \n108            0.909545            0.862416                 0.968112   \n109            0.414198            0.555062                 0.503766   \n110            0.097940            0.099203                 0.063464   \n111            0.922904            0.684354                 0.888287   \n112            0.438602            0.549152                 0.503766   \n\n     X26.5_GR_MaxLong_dd  X26.6_GR_MinLong_dd  X26.7_GR_MidRangeLong_dd  \\\n0               0.169165             0.036599                  0.014533   \n1               0.579751             0.508595                  0.528799   \n2               0.130373             0.320548                  0.169362   \n3               0.491682             0.600141                  0.537498   \n4               0.491682             0.600141                  0.537498   \n..                   ...                  ...                       ...   \n108             1.000000             0.036599                  0.463877   \n109             0.491682             0.600141                  0.537498   \n110             0.902423             0.981855                  0.996300   \n111             1.000000             0.000031                  0.441257   \n112             0.491682             0.600141                  0.537498   \n\n     X27.4_HuPopDen_Change  X28.1_Precip_Mean_mm  X28.2_Temp_Mean_01degC  \\\n0                 0.200000              0.187966                0.194218   \n1                 0.600000              0.170364                0.876430   \n2                 0.300000              0.927452                0.972512   \n3                 0.570000              0.586530                0.709050   \n4                 0.353527              0.590206                0.720736   \n..                     ...                   ...                     ...   \n108               0.071655              0.100771                0.000000   \n109               0.474331              0.590849                0.722581   \n110               0.100000              0.227109                0.556746   \n111               0.250000              0.150321                0.255545   \n112               0.436880              0.590394                0.721987   \n\n     X30.1_AET_Mean_mm  X30.2_PET_Mean_mm  log_HomeRange_km2  \\\n0             0.198967           0.104034           0.369099   \n1             0.288623           0.812691           0.870614   \n2             0.902184           0.837602           0.364136   \n3             0.484833           0.672557           0.731489   \n4             0.472741           0.686570           0.474696   \n..                 ...                ...                ...   \n108           0.090574           0.015616           0.956536   \n109           0.466388           0.673036           0.848956   \n110           0.298893           0.428575           0.392383   \n111           0.166015           0.189559           0.650299   \n112           0.456571           0.687951           0.721192   \n\n     log_HomeRange_Indiv_km2  log_PopulationDensity_n.km2  \\\n0                   0.349669                     0.633553   \n1                   0.828728                     0.024512   \n2                   0.355636                     0.581153   \n3                   0.369072                     0.274731   \n4                   0.347859                     0.703429   \n..                       ...                          ...   \n108                 0.911610                     0.073087   \n109                 0.817994                     0.000000   \n110                 0.355906                     0.575345   \n111                 0.612760                     0.353916   \n112                 0.378023                     0.298662   \n\n     log_HuPopDen_Min_n.km2  log_HuPopDen_Mean_n.km2  log_HuPopDen_5p_n.km2  \\\n0                  0.514713                 0.411510               0.162561   \n1                  0.483852                 0.409161               0.140070   \n2                  0.641462                 0.000000               0.266244   \n3                  0.633855                 0.214465               0.314263   \n4                  0.678321                 0.264731               0.330511   \n..                      ...                      ...                    ...   \n108                0.487184                 0.367537               0.141950   \n109                0.635830                 0.225229               0.298615   \n110                0.335211                 0.448604               0.156351   \n111                0.395564                 0.581328               0.138951   \n112                0.627276                 0.226132               0.336578   \n\n     log_NeonateHeadBodyLen_mm  log_WOS_hits  Afrosoricida  Artiodactyla  \\\n0                     0.273348      0.655406           0.0           0.0   \n1                     0.565604      0.580571           0.0           0.0   \n2                     0.314744      0.407636           0.0           0.0   \n3                     0.866916      0.534133           0.0           0.0   \n4                     0.302667      0.317711           0.0           0.0   \n..                         ...           ...           ...           ...   \n108                   0.665867      0.664675           0.0           0.0   \n109                   0.666788      0.619103           0.0           0.0   \n110                   0.152201      0.443324           0.0           0.0   \n111                   0.537548      0.719847           0.0           0.0   \n112                   0.737874      0.595790           0.0           0.0   \n\n     Carnivora  Cetacea  Chiroptera  Cingulata  Dasyuromorphia  \\\n0          1.0      0.0         0.0        0.0             0.0   \n1          1.0      0.0         0.0        0.0             0.0   \n2          0.0      0.0         0.0        0.0             0.0   \n3          0.0      0.0         0.0        0.0             0.0   \n4          0.0      0.0         0.0        0.0             0.0   \n..         ...      ...         ...        ...             ...   \n108        1.0      0.0         0.0        0.0             0.0   \n109        1.0      0.0         0.0        0.0             0.0   \n110        0.0      0.0         0.0        0.0             0.0   \n111        1.0      0.0         0.0        0.0             0.0   \n112        1.0      0.0         0.0        0.0             0.0   \n\n     Didelphimorphia  Diprotodontia  Erinaceomorpha  Lagomorpha  \\\n0                0.0            0.0             0.0         0.0   \n1                0.0            0.0             0.0         0.0   \n2                0.0            0.0             0.0         0.0   \n3                0.0            0.0             0.0         0.0   \n4                0.0            0.0             0.0         0.0   \n..               ...            ...             ...         ...   \n108              0.0            0.0             0.0         0.0   \n109              0.0            0.0             0.0         0.0   \n110              0.0            0.0             0.0         0.0   \n111              0.0            0.0             0.0         0.0   \n112              0.0            0.0             0.0         0.0   \n\n     Macroscelidea  Monotremata  Perissodactyla  Pholidota  Primates  \\\n0              0.0          0.0             0.0        0.0       0.0   \n1              0.0          0.0             0.0        0.0       0.0   \n2              0.0          0.0             0.0        0.0       1.0   \n3              0.0          0.0             0.0        0.0       0.0   \n4              0.0          0.0             0.0        0.0       0.0   \n..             ...          ...             ...        ...       ...   \n108            0.0          0.0             0.0        0.0       0.0   \n109            0.0          0.0             0.0        0.0       0.0   \n110            0.0          0.0             0.0        0.0       0.0   \n111            0.0          0.0             0.0        0.0       0.0   \n112            0.0          0.0             0.0        0.0       0.0   \n\n     Proboscidea  Rodentia  Sirenia  Soricomorpha  Tubulidentata     nchar  \\\n0            0.0       0.0      0.0           0.0            0.0  0.365854   \n1            0.0       0.0      0.0           0.0            0.0  0.365854   \n2            0.0       0.0      0.0           0.0            0.0  0.365854   \n3            0.0       0.0      0.0           0.0            0.0  0.341463   \n4            0.0       0.0      0.0           0.0            0.0  0.341463   \n..           ...       ...      ...           ...            ...       ...   \n108          0.0       0.0      0.0           0.0            0.0  0.365854   \n109          0.0       0.0      0.0           0.0            0.0  0.000000   \n110          0.0       0.0      0.0           0.0            0.0  0.463415   \n111          0.0       0.0      0.0           0.0            0.0  0.341463   \n112          0.0       0.0      0.0           0.0            0.0  0.390244   \n\n     haddock_score_sd  AA_83_Y  AA_30_negative  binary_haddock_score  \n0            0.160701      1.0             1.0                   1.0  \n1            0.212083      1.0             1.0                   0.0  \n2            0.482780      1.0             1.0                   1.0  \n3            0.290665      1.0             0.0                   0.0  \n4            0.548594      1.0             1.0                   0.0  \n..                ...      ...             ...                   ...  \n108          0.712780      1.0             1.0                   1.0  \n109          0.405247      1.0             1.0                   1.0  \n110          0.725709      0.0             1.0                   0.0  \n111          1.000000      1.0             1.0                   1.0  \n112          0.774647      1.0             1.0                   1.0  \n\n[113 rows x 76 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ForStrat_terrestrial</th>\n      <th>ForStrat_aquatic</th>\n      <th>Activity.Nocturnal</th>\n      <th>Activity.Crepuscular</th>\n      <th>Activity.Diurnal</th>\n      <th>female_maturity_d</th>\n      <th>male_maturity_d</th>\n      <th>weaning_d</th>\n      <th>litters_or_clutches_per_y</th>\n      <th>longevity_y</th>\n      <th>adult_svl_cm</th>\n      <th>metabolicRate_W</th>\n      <th>temperature_K</th>\n      <th>diet_breadth</th>\n      <th>development_d</th>\n      <th>mass_specific_production</th>\n      <th>log_litterclutch_size_n</th>\n      <th>log_inter_litterbirth_interval_y</th>\n      <th>log_birthhatching_weight_g</th>\n      <th>log_weaning_weight_g</th>\n      <th>log_adult_body_mass_g</th>\n      <th>log_female_body_mass_g</th>\n      <th>log_male_body_mass_g</th>\n      <th>tnc_ecoregion_breadth</th>\n      <th>log_range_size</th>\n      <th>X2.1_AgeatEyeOpening_d</th>\n      <th>X9.1_GestationLen_d</th>\n      <th>X10.2_SocialGrpSize</th>\n      <th>X24.1_TeatNumber</th>\n      <th>X6.2_TrophicLevel</th>\n      <th>X13.3_WeaningHeadBodyLen_mm</th>\n      <th>X26.2_GR_MaxLat_dd</th>\n      <th>X26.3_GR_MinLat_dd</th>\n      <th>X26.4_GR_MidRangeLat_dd</th>\n      <th>X26.5_GR_MaxLong_dd</th>\n      <th>X26.6_GR_MinLong_dd</th>\n      <th>X26.7_GR_MidRangeLong_dd</th>\n      <th>X27.4_HuPopDen_Change</th>\n      <th>X28.1_Precip_Mean_mm</th>\n      <th>X28.2_Temp_Mean_01degC</th>\n      <th>X30.1_AET_Mean_mm</th>\n      <th>X30.2_PET_Mean_mm</th>\n      <th>log_HomeRange_km2</th>\n      <th>log_HomeRange_Indiv_km2</th>\n      <th>log_PopulationDensity_n.km2</th>\n      <th>log_HuPopDen_Min_n.km2</th>\n      <th>log_HuPopDen_Mean_n.km2</th>\n      <th>log_HuPopDen_5p_n.km2</th>\n      <th>log_NeonateHeadBodyLen_mm</th>\n      <th>log_WOS_hits</th>\n      <th>Afrosoricida</th>\n      <th>Artiodactyla</th>\n      <th>Carnivora</th>\n      <th>Cetacea</th>\n      <th>Chiroptera</th>\n      <th>Cingulata</th>\n      <th>Dasyuromorphia</th>\n      <th>Didelphimorphia</th>\n      <th>Diprotodontia</th>\n      <th>Erinaceomorpha</th>\n      <th>Lagomorpha</th>\n      <th>Macroscelidea</th>\n      <th>Monotremata</th>\n      <th>Perissodactyla</th>\n      <th>Pholidota</th>\n      <th>Primates</th>\n      <th>Proboscidea</th>\n      <th>Rodentia</th>\n      <th>Sirenia</th>\n      <th>Soricomorpha</th>\n      <th>Tubulidentata</th>\n      <th>nchar</th>\n      <th>haddock_score_sd</th>\n      <th>AA_83_Y</th>\n      <th>AA_30_negative</th>\n      <th>binary_haddock_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.065797</td>\n      <td>0.075841</td>\n      <td>0.034741</td>\n      <td>0.210526</td>\n      <td>0.138029</td>\n      <td>0.067902</td>\n      <td>0.027137</td>\n      <td>0.864198</td>\n      <td>0.60</td>\n      <td>0.047435</td>\n      <td>0.064324</td>\n      <td>0.712741</td>\n      <td>0.625904</td>\n      <td>0.344187</td>\n      <td>0.255546</td>\n      <td>0.357657</td>\n      <td>0.497357</td>\n      <td>0.429557</td>\n      <td>0.487179</td>\n      <td>0.741971</td>\n      <td>0.571579</td>\n      <td>0.051922</td>\n      <td>0.009375</td>\n      <td>0.54</td>\n      <td>0.8</td>\n      <td>0.294645</td>\n      <td>0.901577</td>\n      <td>0.818372</td>\n      <td>0.941017</td>\n      <td>0.169165</td>\n      <td>0.036599</td>\n      <td>0.014533</td>\n      <td>0.200000</td>\n      <td>0.187966</td>\n      <td>0.194218</td>\n      <td>0.198967</td>\n      <td>0.104034</td>\n      <td>0.369099</td>\n      <td>0.349669</td>\n      <td>0.633553</td>\n      <td>0.514713</td>\n      <td>0.411510</td>\n      <td>0.162561</td>\n      <td>0.273348</td>\n      <td>0.655406</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365854</td>\n      <td>0.160701</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.144858</td>\n      <td>0.095436</td>\n      <td>0.104223</td>\n      <td>0.131579</td>\n      <td>0.269841</td>\n      <td>0.257353</td>\n      <td>0.627047</td>\n      <td>0.864198</td>\n      <td>0.00</td>\n      <td>0.164926</td>\n      <td>0.047067</td>\n      <td>0.510529</td>\n      <td>0.719685</td>\n      <td>0.573453</td>\n      <td>0.473731</td>\n      <td>0.635173</td>\n      <td>0.756037</td>\n      <td>0.687300</td>\n      <td>0.320513</td>\n      <td>0.647623</td>\n      <td>0.237632</td>\n      <td>0.170946</td>\n      <td>0.245312</td>\n      <td>1.00</td>\n      <td>1.0</td>\n      <td>0.294645</td>\n      <td>0.630613</td>\n      <td>0.250419</td>\n      <td>0.483326</td>\n      <td>0.579751</td>\n      <td>0.508595</td>\n      <td>0.528799</td>\n      <td>0.600000</td>\n      <td>0.170364</td>\n      <td>0.876430</td>\n      <td>0.288623</td>\n      <td>0.812691</td>\n      <td>0.870614</td>\n      <td>0.828728</td>\n      <td>0.024512</td>\n      <td>0.483852</td>\n      <td>0.409161</td>\n      <td>0.140070</td>\n      <td>0.565604</td>\n      <td>0.580571</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365854</td>\n      <td>0.212083</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.167050</td>\n      <td>0.134908</td>\n      <td>0.215298</td>\n      <td>0.308798</td>\n      <td>0.149074</td>\n      <td>0.057181</td>\n      <td>0.019523</td>\n      <td>0.518519</td>\n      <td>1.00</td>\n      <td>0.250944</td>\n      <td>0.135447</td>\n      <td>0.018041</td>\n      <td>0.598573</td>\n      <td>0.466081</td>\n      <td>0.364180</td>\n      <td>0.348787</td>\n      <td>0.536702</td>\n      <td>0.376954</td>\n      <td>0.076923</td>\n      <td>0.436531</td>\n      <td>0.315316</td>\n      <td>0.206166</td>\n      <td>0.046875</td>\n      <td>0.20</td>\n      <td>0.5</td>\n      <td>0.294645</td>\n      <td>0.305167</td>\n      <td>0.488989</td>\n      <td>0.391309</td>\n      <td>0.130373</td>\n      <td>0.320548</td>\n      <td>0.169362</td>\n      <td>0.300000</td>\n      <td>0.927452</td>\n      <td>0.972512</td>\n      <td>0.902184</td>\n      <td>0.837602</td>\n      <td>0.364136</td>\n      <td>0.355636</td>\n      <td>0.581153</td>\n      <td>0.641462</td>\n      <td>0.000000</td>\n      <td>0.266244</td>\n      <td>0.314744</td>\n      <td>0.407636</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365854</td>\n      <td>0.482780</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.706728</td>\n      <td>0.621946</td>\n      <td>0.176509</td>\n      <td>0.210526</td>\n      <td>0.738095</td>\n      <td>0.650588</td>\n      <td>0.540504</td>\n      <td>0.587654</td>\n      <td>0.20</td>\n      <td>0.654653</td>\n      <td>0.062315</td>\n      <td>0.009111</td>\n      <td>0.685771</td>\n      <td>0.949202</td>\n      <td>1.000000</td>\n      <td>0.979500</td>\n      <td>0.853961</td>\n      <td>0.949329</td>\n      <td>0.589744</td>\n      <td>0.943420</td>\n      <td>0.047526</td>\n      <td>0.625145</td>\n      <td>0.015625</td>\n      <td>0.24</td>\n      <td>1.0</td>\n      <td>0.294645</td>\n      <td>0.420887</td>\n      <td>0.562710</td>\n      <td>0.503766</td>\n      <td>0.491682</td>\n      <td>0.600141</td>\n      <td>0.537498</td>\n      <td>0.570000</td>\n      <td>0.586530</td>\n      <td>0.709050</td>\n      <td>0.484833</td>\n      <td>0.672557</td>\n      <td>0.731489</td>\n      <td>0.369072</td>\n      <td>0.274731</td>\n      <td>0.633855</td>\n      <td>0.214465</td>\n      <td>0.314263</td>\n      <td>0.866916</td>\n      <td>0.534133</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.341463</td>\n      <td>0.290665</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.117562</td>\n      <td>0.152370</td>\n      <td>0.084097</td>\n      <td>0.228638</td>\n      <td>0.146755</td>\n      <td>0.059387</td>\n      <td>0.024461</td>\n      <td>0.397531</td>\n      <td>0.04</td>\n      <td>0.233807</td>\n      <td>0.303096</td>\n      <td>0.246598</td>\n      <td>0.585111</td>\n      <td>0.388389</td>\n      <td>0.291012</td>\n      <td>0.143948</td>\n      <td>0.336066</td>\n      <td>0.325862</td>\n      <td>0.038462</td>\n      <td>0.432175</td>\n      <td>0.165789</td>\n      <td>0.206341</td>\n      <td>0.026719</td>\n      <td>0.22</td>\n      <td>0.6</td>\n      <td>0.294645</td>\n      <td>0.437272</td>\n      <td>0.548509</td>\n      <td>0.503766</td>\n      <td>0.491682</td>\n      <td>0.600141</td>\n      <td>0.537498</td>\n      <td>0.353527</td>\n      <td>0.590206</td>\n      <td>0.720736</td>\n      <td>0.472741</td>\n      <td>0.686570</td>\n      <td>0.474696</td>\n      <td>0.347859</td>\n      <td>0.703429</td>\n      <td>0.678321</td>\n      <td>0.264731</td>\n      <td>0.330511</td>\n      <td>0.302667</td>\n      <td>0.317711</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.341463</td>\n      <td>0.548594</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.334200</td>\n      <td>0.292992</td>\n      <td>0.231208</td>\n      <td>0.052632</td>\n      <td>0.658730</td>\n      <td>0.399374</td>\n      <td>0.261247</td>\n      <td>0.654321</td>\n      <td>0.60</td>\n      <td>0.195922</td>\n      <td>0.035384</td>\n      <td>0.378810</td>\n      <td>0.835007</td>\n      <td>0.577584</td>\n      <td>0.671043</td>\n      <td>0.742567</td>\n      <td>0.841876</td>\n      <td>0.768516</td>\n      <td>0.525641</td>\n      <td>0.824058</td>\n      <td>0.837368</td>\n      <td>0.460971</td>\n      <td>0.442969</td>\n      <td>0.56</td>\n      <td>0.5</td>\n      <td>0.294645</td>\n      <td>0.909545</td>\n      <td>0.862416</td>\n      <td>0.968112</td>\n      <td>1.000000</td>\n      <td>0.036599</td>\n      <td>0.463877</td>\n      <td>0.071655</td>\n      <td>0.100771</td>\n      <td>0.000000</td>\n      <td>0.090574</td>\n      <td>0.015616</td>\n      <td>0.956536</td>\n      <td>0.911610</td>\n      <td>0.073087</td>\n      <td>0.487184</td>\n      <td>0.367537</td>\n      <td>0.141950</td>\n      <td>0.665867</td>\n      <td>0.664675</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365854</td>\n      <td>0.712780</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.442722</td>\n      <td>0.390041</td>\n      <td>0.367106</td>\n      <td>0.052632</td>\n      <td>0.663095</td>\n      <td>0.392454</td>\n      <td>0.456740</td>\n      <td>0.592593</td>\n      <td>0.20</td>\n      <td>0.203055</td>\n      <td>0.034904</td>\n      <td>0.237672</td>\n      <td>0.809091</td>\n      <td>0.592642</td>\n      <td>0.642024</td>\n      <td>0.772554</td>\n      <td>0.872286</td>\n      <td>0.830967</td>\n      <td>0.166667</td>\n      <td>0.781556</td>\n      <td>0.897895</td>\n      <td>0.111835</td>\n      <td>0.271875</td>\n      <td>0.20</td>\n      <td>0.5</td>\n      <td>0.294645</td>\n      <td>0.414198</td>\n      <td>0.555062</td>\n      <td>0.503766</td>\n      <td>0.491682</td>\n      <td>0.600141</td>\n      <td>0.537498</td>\n      <td>0.474331</td>\n      <td>0.590849</td>\n      <td>0.722581</td>\n      <td>0.466388</td>\n      <td>0.673036</td>\n      <td>0.848956</td>\n      <td>0.817994</td>\n      <td>0.000000</td>\n      <td>0.635830</td>\n      <td>0.225229</td>\n      <td>0.298615</td>\n      <td>0.666788</td>\n      <td>0.619103</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.405247</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.182541</td>\n      <td>0.158598</td>\n      <td>0.453702</td>\n      <td>0.150000</td>\n      <td>0.382540</td>\n      <td>0.186444</td>\n      <td>0.111785</td>\n      <td>0.360494</td>\n      <td>0.00</td>\n      <td>0.028600</td>\n      <td>0.034147</td>\n      <td>0.009111</td>\n      <td>0.790126</td>\n      <td>0.179724</td>\n      <td>0.555667</td>\n      <td>0.588676</td>\n      <td>0.767786</td>\n      <td>0.713814</td>\n      <td>0.089744</td>\n      <td>0.394868</td>\n      <td>0.092263</td>\n      <td>0.037550</td>\n      <td>0.000000</td>\n      <td>0.16</td>\n      <td>0.0</td>\n      <td>0.294645</td>\n      <td>0.097940</td>\n      <td>0.099203</td>\n      <td>0.063464</td>\n      <td>0.902423</td>\n      <td>0.981855</td>\n      <td>0.996300</td>\n      <td>0.100000</td>\n      <td>0.227109</td>\n      <td>0.556746</td>\n      <td>0.298893</td>\n      <td>0.428575</td>\n      <td>0.392383</td>\n      <td>0.355906</td>\n      <td>0.575345</td>\n      <td>0.335211</td>\n      <td>0.448604</td>\n      <td>0.156351</td>\n      <td>0.152201</td>\n      <td>0.443324</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.463415</td>\n      <td>0.725709</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.067445</td>\n      <td>0.060396</td>\n      <td>0.035592</td>\n      <td>0.210526</td>\n      <td>0.206349</td>\n      <td>0.103641</td>\n      <td>0.138985</td>\n      <td>0.827160</td>\n      <td>0.40</td>\n      <td>0.082913</td>\n      <td>0.102708</td>\n      <td>0.710128</td>\n      <td>0.625904</td>\n      <td>0.485304</td>\n      <td>0.448767</td>\n      <td>0.469748</td>\n      <td>0.678059</td>\n      <td>0.523919</td>\n      <td>0.653846</td>\n      <td>0.886529</td>\n      <td>0.368684</td>\n      <td>0.085773</td>\n      <td>0.003906</td>\n      <td>0.60</td>\n      <td>1.0</td>\n      <td>0.038910</td>\n      <td>0.922904</td>\n      <td>0.684354</td>\n      <td>0.888287</td>\n      <td>1.000000</td>\n      <td>0.000031</td>\n      <td>0.441257</td>\n      <td>0.250000</td>\n      <td>0.150321</td>\n      <td>0.255545</td>\n      <td>0.166015</td>\n      <td>0.189559</td>\n      <td>0.650299</td>\n      <td>0.612760</td>\n      <td>0.353916</td>\n      <td>0.395564</td>\n      <td>0.581328</td>\n      <td>0.138951</td>\n      <td>0.537548</td>\n      <td>0.719847</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.341463</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.419004</td>\n      <td>0.411249</td>\n      <td>0.353401</td>\n      <td>0.181579</td>\n      <td>0.444444</td>\n      <td>0.394814</td>\n      <td>0.539606</td>\n      <td>0.753086</td>\n      <td>0.20</td>\n      <td>0.627112</td>\n      <td>0.061690</td>\n      <td>0.052093</td>\n      <td>0.790126</td>\n      <td>0.727346</td>\n      <td>0.668061</td>\n      <td>0.713890</td>\n      <td>0.916977</td>\n      <td>0.824376</td>\n      <td>0.038462</td>\n      <td>0.545340</td>\n      <td>0.000000</td>\n      <td>0.723370</td>\n      <td>0.142813</td>\n      <td>0.12</td>\n      <td>0.5</td>\n      <td>0.294645</td>\n      <td>0.438602</td>\n      <td>0.549152</td>\n      <td>0.503766</td>\n      <td>0.491682</td>\n      <td>0.600141</td>\n      <td>0.537498</td>\n      <td>0.436880</td>\n      <td>0.590394</td>\n      <td>0.721987</td>\n      <td>0.456571</td>\n      <td>0.687951</td>\n      <td>0.721192</td>\n      <td>0.378023</td>\n      <td>0.298662</td>\n      <td>0.627276</td>\n      <td>0.226132</td>\n      <td>0.336578</td>\n      <td>0.737874</td>\n      <td>0.595790</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.390244</td>\n      <td>0.774647</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>113 rows × 76 columns</p>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# escape to get the data normalized back, instead of the need to go back all the way to the beginning\n",
    "tmp = data\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:50.877638347Z",
     "start_time": "2023-10-05T18:31:50.583419640Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     ForStrat_terrestrial  ForStrat_aquatic  Activity.Nocturnal  \\\n0                     1.0               0.0                 1.0   \n1                     1.0               0.0                 0.0   \n2                     1.0               0.0                 1.0   \n3                     0.0               1.0                 0.0   \n4                     1.0               0.0                 1.0   \n..                    ...               ...                 ...   \n108                   1.0               0.0                 0.0   \n109                   1.0               0.0                 0.0   \n110                   1.0               0.0                 1.0   \n111                   1.0               0.0                 1.0   \n112                   0.0               1.0                 1.0   \n\n     Activity.Crepuscular  Activity.Diurnal  female_maturity_d  weaning_d  \\\n0                     1.0               0.0           0.065797   0.034741   \n1                     0.0               1.0           0.144858   0.104223   \n2                     0.0               0.0           0.167050   0.215298   \n3                     0.0               1.0           0.706728   0.176509   \n4                     0.0               0.0           0.117562   0.084097   \n..                    ...               ...                ...        ...   \n108                   1.0               1.0           0.334200   0.231208   \n109                   1.0               1.0           0.442722   0.367106   \n110                   0.0               0.0           0.182541   0.453702   \n111                   1.0               0.0           0.067445   0.035592   \n112                   1.0               0.0           0.419004   0.353401   \n\n     litters_or_clutches_per_y  longevity_y  adult_svl_cm  development_d  \\\n0                     0.210526     0.138029      0.067902       0.047435   \n1                     0.131579     0.269841      0.257353       0.164926   \n2                     0.308798     0.149074      0.057181       0.250944   \n3                     0.210526     0.738095      0.650588       0.654653   \n4                     0.228638     0.146755      0.059387       0.233807   \n..                         ...          ...           ...            ...   \n108                   0.052632     0.658730      0.399374       0.195922   \n109                   0.052632     0.663095      0.392454       0.203055   \n110                   0.150000     0.382540      0.186444       0.028600   \n111                   0.210526     0.206349      0.103641       0.082913   \n112                   0.181579     0.444444      0.394814       0.627112   \n\n     log_litterclutch_size_n  log_birthhatching_weight_g  \\\n0                   0.712741                    0.344187   \n1                   0.510529                    0.573453   \n2                   0.018041                    0.466081   \n3                   0.009111                    0.949202   \n4                   0.246598                    0.388389   \n..                       ...                         ...   \n108                 0.378810                    0.577584   \n109                 0.237672                    0.592642   \n110                 0.009111                    0.179724   \n111                 0.710128                    0.485304   \n112                 0.052093                    0.727346   \n\n     log_weaning_weight_g  log_male_body_mass_g  log_range_size  \\\n0                0.255546              0.429557        0.741971   \n1                0.473731              0.687300        0.647623   \n2                0.364180              0.376954        0.436531   \n3                1.000000              0.949329        0.943420   \n4                0.291012              0.325862        0.432175   \n..                    ...                   ...             ...   \n108              0.671043              0.768516        0.824058   \n109              0.642024              0.830967        0.781556   \n110              0.555667              0.713814        0.394868   \n111              0.448767              0.523919        0.886529   \n112              0.668061              0.824376        0.545340   \n\n     X2.1_AgeatEyeOpening_d  X9.1_GestationLen_d  X10.2_SocialGrpSize  \\\n0                  0.571579             0.051922             0.009375   \n1                  0.237632             0.170946             0.245312   \n2                  0.315316             0.206166             0.046875   \n3                  0.047526             0.625145             0.015625   \n4                  0.165789             0.206341             0.026719   \n..                      ...                  ...                  ...   \n108                0.837368             0.460971             0.442969   \n109                0.897895             0.111835             0.271875   \n110                0.092263             0.037550             0.000000   \n111                0.368684             0.085773             0.003906   \n112                0.000000             0.723370             0.142813   \n\n     X24.1_TeatNumber  X6.2_TrophicLevel  X26.2_GR_MaxLat_dd  \\\n0                0.54                0.8            0.901577   \n1                1.00                1.0            0.630613   \n2                0.20                0.5            0.305167   \n3                0.24                1.0            0.420887   \n4                0.22                0.6            0.437272   \n..                ...                ...                 ...   \n108              0.56                0.5            0.909545   \n109              0.20                0.5            0.414198   \n110              0.16                0.0            0.097940   \n111              0.60                1.0            0.922904   \n112              0.12                0.5            0.438602   \n\n     X26.3_GR_MinLat_dd  X26.6_GR_MinLong_dd  X28.1_Precip_Mean_mm  \\\n0              0.818372             0.036599              0.187966   \n1              0.250419             0.508595              0.170364   \n2              0.488989             0.320548              0.927452   \n3              0.562710             0.600141              0.586530   \n4              0.548509             0.600141              0.590206   \n..                  ...                  ...                   ...   \n108            0.862416             0.036599              0.100771   \n109            0.555062             0.600141              0.590849   \n110            0.099203             0.981855              0.227109   \n111            0.684354             0.000031              0.150321   \n112            0.549152             0.600141              0.590394   \n\n     X28.2_Temp_Mean_01degC  X30.1_AET_Mean_mm  log_HomeRange_km2  \\\n0                  0.194218           0.198967           0.369099   \n1                  0.876430           0.288623           0.870614   \n2                  0.972512           0.902184           0.364136   \n3                  0.709050           0.484833           0.731489   \n4                  0.720736           0.472741           0.474696   \n..                      ...                ...                ...   \n108                0.000000           0.090574           0.956536   \n109                0.722581           0.466388           0.848956   \n110                0.556746           0.298893           0.392383   \n111                0.255545           0.166015           0.650299   \n112                0.721987           0.456571           0.721192   \n\n     log_HuPopDen_Mean_n.km2  log_NeonateHeadBodyLen_mm  Cingulata  \\\n0                   0.411510                   0.273348        0.0   \n1                   0.409161                   0.565604        0.0   \n2                   0.000000                   0.314744        0.0   \n3                   0.214465                   0.866916        0.0   \n4                   0.264731                   0.302667        0.0   \n..                       ...                        ...        ...   \n108                 0.367537                   0.665867        0.0   \n109                 0.225229                   0.666788        0.0   \n110                 0.448604                   0.152201        0.0   \n111                 0.581328                   0.537548        0.0   \n112                 0.226132                   0.737874        0.0   \n\n     Didelphimorphia  Erinaceomorpha  Perissodactyla  Pholidota  Primates  \\\n0                0.0             0.0             0.0        0.0       0.0   \n1                0.0             0.0             0.0        0.0       0.0   \n2                0.0             0.0             0.0        0.0       1.0   \n3                0.0             0.0             0.0        0.0       0.0   \n4                0.0             0.0             0.0        0.0       0.0   \n..               ...             ...             ...        ...       ...   \n108              0.0             0.0             0.0        0.0       0.0   \n109              0.0             0.0             0.0        0.0       0.0   \n110              0.0             0.0             0.0        0.0       0.0   \n111              0.0             0.0             0.0        0.0       0.0   \n112              0.0             0.0             0.0        0.0       0.0   \n\n     Rodentia  Soricomorpha  AA_83_Y  AA_30_negative  binary_haddock_score  \n0         0.0           0.0      1.0             1.0                   1.0  \n1         0.0           0.0      1.0             1.0                   0.0  \n2         0.0           0.0      1.0             1.0                   1.0  \n3         0.0           0.0      1.0             0.0                   0.0  \n4         0.0           0.0      1.0             1.0                   0.0  \n..        ...           ...      ...             ...                   ...  \n108       0.0           0.0      1.0             1.0                   1.0  \n109       0.0           0.0      1.0             1.0                   1.0  \n110       0.0           0.0      0.0             1.0                   0.0  \n111       0.0           0.0      1.0             1.0                   1.0  \n112       0.0           0.0      1.0             1.0                   1.0  \n\n[113 rows x 41 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ForStrat_terrestrial</th>\n      <th>ForStrat_aquatic</th>\n      <th>Activity.Nocturnal</th>\n      <th>Activity.Crepuscular</th>\n      <th>Activity.Diurnal</th>\n      <th>female_maturity_d</th>\n      <th>weaning_d</th>\n      <th>litters_or_clutches_per_y</th>\n      <th>longevity_y</th>\n      <th>adult_svl_cm</th>\n      <th>development_d</th>\n      <th>log_litterclutch_size_n</th>\n      <th>log_birthhatching_weight_g</th>\n      <th>log_weaning_weight_g</th>\n      <th>log_male_body_mass_g</th>\n      <th>log_range_size</th>\n      <th>X2.1_AgeatEyeOpening_d</th>\n      <th>X9.1_GestationLen_d</th>\n      <th>X10.2_SocialGrpSize</th>\n      <th>X24.1_TeatNumber</th>\n      <th>X6.2_TrophicLevel</th>\n      <th>X26.2_GR_MaxLat_dd</th>\n      <th>X26.3_GR_MinLat_dd</th>\n      <th>X26.6_GR_MinLong_dd</th>\n      <th>X28.1_Precip_Mean_mm</th>\n      <th>X28.2_Temp_Mean_01degC</th>\n      <th>X30.1_AET_Mean_mm</th>\n      <th>log_HomeRange_km2</th>\n      <th>log_HuPopDen_Mean_n.km2</th>\n      <th>log_NeonateHeadBodyLen_mm</th>\n      <th>Cingulata</th>\n      <th>Didelphimorphia</th>\n      <th>Erinaceomorpha</th>\n      <th>Perissodactyla</th>\n      <th>Pholidota</th>\n      <th>Primates</th>\n      <th>Rodentia</th>\n      <th>Soricomorpha</th>\n      <th>AA_83_Y</th>\n      <th>AA_30_negative</th>\n      <th>binary_haddock_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.065797</td>\n      <td>0.034741</td>\n      <td>0.210526</td>\n      <td>0.138029</td>\n      <td>0.067902</td>\n      <td>0.047435</td>\n      <td>0.712741</td>\n      <td>0.344187</td>\n      <td>0.255546</td>\n      <td>0.429557</td>\n      <td>0.741971</td>\n      <td>0.571579</td>\n      <td>0.051922</td>\n      <td>0.009375</td>\n      <td>0.54</td>\n      <td>0.8</td>\n      <td>0.901577</td>\n      <td>0.818372</td>\n      <td>0.036599</td>\n      <td>0.187966</td>\n      <td>0.194218</td>\n      <td>0.198967</td>\n      <td>0.369099</td>\n      <td>0.411510</td>\n      <td>0.273348</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.144858</td>\n      <td>0.104223</td>\n      <td>0.131579</td>\n      <td>0.269841</td>\n      <td>0.257353</td>\n      <td>0.164926</td>\n      <td>0.510529</td>\n      <td>0.573453</td>\n      <td>0.473731</td>\n      <td>0.687300</td>\n      <td>0.647623</td>\n      <td>0.237632</td>\n      <td>0.170946</td>\n      <td>0.245312</td>\n      <td>1.00</td>\n      <td>1.0</td>\n      <td>0.630613</td>\n      <td>0.250419</td>\n      <td>0.508595</td>\n      <td>0.170364</td>\n      <td>0.876430</td>\n      <td>0.288623</td>\n      <td>0.870614</td>\n      <td>0.409161</td>\n      <td>0.565604</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.167050</td>\n      <td>0.215298</td>\n      <td>0.308798</td>\n      <td>0.149074</td>\n      <td>0.057181</td>\n      <td>0.250944</td>\n      <td>0.018041</td>\n      <td>0.466081</td>\n      <td>0.364180</td>\n      <td>0.376954</td>\n      <td>0.436531</td>\n      <td>0.315316</td>\n      <td>0.206166</td>\n      <td>0.046875</td>\n      <td>0.20</td>\n      <td>0.5</td>\n      <td>0.305167</td>\n      <td>0.488989</td>\n      <td>0.320548</td>\n      <td>0.927452</td>\n      <td>0.972512</td>\n      <td>0.902184</td>\n      <td>0.364136</td>\n      <td>0.000000</td>\n      <td>0.314744</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.706728</td>\n      <td>0.176509</td>\n      <td>0.210526</td>\n      <td>0.738095</td>\n      <td>0.650588</td>\n      <td>0.654653</td>\n      <td>0.009111</td>\n      <td>0.949202</td>\n      <td>1.000000</td>\n      <td>0.949329</td>\n      <td>0.943420</td>\n      <td>0.047526</td>\n      <td>0.625145</td>\n      <td>0.015625</td>\n      <td>0.24</td>\n      <td>1.0</td>\n      <td>0.420887</td>\n      <td>0.562710</td>\n      <td>0.600141</td>\n      <td>0.586530</td>\n      <td>0.709050</td>\n      <td>0.484833</td>\n      <td>0.731489</td>\n      <td>0.214465</td>\n      <td>0.866916</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.117562</td>\n      <td>0.084097</td>\n      <td>0.228638</td>\n      <td>0.146755</td>\n      <td>0.059387</td>\n      <td>0.233807</td>\n      <td>0.246598</td>\n      <td>0.388389</td>\n      <td>0.291012</td>\n      <td>0.325862</td>\n      <td>0.432175</td>\n      <td>0.165789</td>\n      <td>0.206341</td>\n      <td>0.026719</td>\n      <td>0.22</td>\n      <td>0.6</td>\n      <td>0.437272</td>\n      <td>0.548509</td>\n      <td>0.600141</td>\n      <td>0.590206</td>\n      <td>0.720736</td>\n      <td>0.472741</td>\n      <td>0.474696</td>\n      <td>0.264731</td>\n      <td>0.302667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.334200</td>\n      <td>0.231208</td>\n      <td>0.052632</td>\n      <td>0.658730</td>\n      <td>0.399374</td>\n      <td>0.195922</td>\n      <td>0.378810</td>\n      <td>0.577584</td>\n      <td>0.671043</td>\n      <td>0.768516</td>\n      <td>0.824058</td>\n      <td>0.837368</td>\n      <td>0.460971</td>\n      <td>0.442969</td>\n      <td>0.56</td>\n      <td>0.5</td>\n      <td>0.909545</td>\n      <td>0.862416</td>\n      <td>0.036599</td>\n      <td>0.100771</td>\n      <td>0.000000</td>\n      <td>0.090574</td>\n      <td>0.956536</td>\n      <td>0.367537</td>\n      <td>0.665867</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.442722</td>\n      <td>0.367106</td>\n      <td>0.052632</td>\n      <td>0.663095</td>\n      <td>0.392454</td>\n      <td>0.203055</td>\n      <td>0.237672</td>\n      <td>0.592642</td>\n      <td>0.642024</td>\n      <td>0.830967</td>\n      <td>0.781556</td>\n      <td>0.897895</td>\n      <td>0.111835</td>\n      <td>0.271875</td>\n      <td>0.20</td>\n      <td>0.5</td>\n      <td>0.414198</td>\n      <td>0.555062</td>\n      <td>0.600141</td>\n      <td>0.590849</td>\n      <td>0.722581</td>\n      <td>0.466388</td>\n      <td>0.848956</td>\n      <td>0.225229</td>\n      <td>0.666788</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.182541</td>\n      <td>0.453702</td>\n      <td>0.150000</td>\n      <td>0.382540</td>\n      <td>0.186444</td>\n      <td>0.028600</td>\n      <td>0.009111</td>\n      <td>0.179724</td>\n      <td>0.555667</td>\n      <td>0.713814</td>\n      <td>0.394868</td>\n      <td>0.092263</td>\n      <td>0.037550</td>\n      <td>0.000000</td>\n      <td>0.16</td>\n      <td>0.0</td>\n      <td>0.097940</td>\n      <td>0.099203</td>\n      <td>0.981855</td>\n      <td>0.227109</td>\n      <td>0.556746</td>\n      <td>0.298893</td>\n      <td>0.392383</td>\n      <td>0.448604</td>\n      <td>0.152201</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.067445</td>\n      <td>0.035592</td>\n      <td>0.210526</td>\n      <td>0.206349</td>\n      <td>0.103641</td>\n      <td>0.082913</td>\n      <td>0.710128</td>\n      <td>0.485304</td>\n      <td>0.448767</td>\n      <td>0.523919</td>\n      <td>0.886529</td>\n      <td>0.368684</td>\n      <td>0.085773</td>\n      <td>0.003906</td>\n      <td>0.60</td>\n      <td>1.0</td>\n      <td>0.922904</td>\n      <td>0.684354</td>\n      <td>0.000031</td>\n      <td>0.150321</td>\n      <td>0.255545</td>\n      <td>0.166015</td>\n      <td>0.650299</td>\n      <td>0.581328</td>\n      <td>0.537548</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.419004</td>\n      <td>0.353401</td>\n      <td>0.181579</td>\n      <td>0.444444</td>\n      <td>0.394814</td>\n      <td>0.627112</td>\n      <td>0.052093</td>\n      <td>0.727346</td>\n      <td>0.668061</td>\n      <td>0.824376</td>\n      <td>0.545340</td>\n      <td>0.000000</td>\n      <td>0.723370</td>\n      <td>0.142813</td>\n      <td>0.12</td>\n      <td>0.5</td>\n      <td>0.438602</td>\n      <td>0.549152</td>\n      <td>0.600141</td>\n      <td>0.590394</td>\n      <td>0.721987</td>\n      <td>0.456571</td>\n      <td>0.721192</td>\n      <td>0.226132</td>\n      <td>0.737874</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>113 rows × 41 columns</p>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attributes selection\n",
    "chosen_attributes = [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 14, 16, 18, 19, 22, 24, 25, 26, 27, 28, 29, 31, 32, 35, 38, 39, \n",
    "                     40, 42, 46, 48, 55, 57, 59, 63, 64, 65, 67, 69, 73, 74, 75]\n",
    "\n",
    "data = data.iloc[:, chosen_attributes]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:51.127879940Z",
     "start_time": "2023-10-05T18:31:50.650725998Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "    ForStrat_terrestrial  ForStrat_aquatic  Activity.Nocturnal  \\\n1                    1.0               0.0                 0.0   \n3                    0.0               1.0                 0.0   \n4                    1.0               0.0                 1.0   \n5                    1.0               0.0                 0.0   \n6                    0.0               1.0                 1.0   \n..                   ...               ...                 ...   \n77                   1.0               0.0                 0.0   \n78                   1.0               0.0                 1.0   \n80                   1.0               0.0                 1.0   \n81                   1.0               0.0                 1.0   \n82                   1.0               0.0                 1.0   \n\n    Activity.Crepuscular  Activity.Diurnal  female_maturity_d  weaning_d  \\\n1                    0.0               1.0           0.144858   0.104223   \n3                    0.0               1.0           0.706728   0.176509   \n4                    0.0               0.0           0.117562   0.084097   \n5                    0.0               1.0           0.112845   0.050315   \n6                    1.0               0.0           0.371078   0.104223   \n..                   ...               ...                ...        ...   \n77                   0.0               1.0           0.403143   0.382105   \n78                   0.0               0.0           0.006276   0.002791   \n80                   0.0               0.0           0.157196   0.385747   \n81                   1.0               0.0           0.018633   0.000000   \n82                   0.0               0.0           0.121748   0.019205   \n\n    litters_or_clutches_per_y  longevity_y  adult_svl_cm  development_d  \\\n1                    0.131579     0.269841      0.257353       0.164926   \n3                    0.210526     0.738095      0.650588       0.654653   \n4                    0.228638     0.146755      0.059387       0.233807   \n5                    0.473684     0.226190      0.044673       0.287087   \n6                    0.130263     0.380952      0.336369       0.516945   \n..                        ...          ...           ...            ...   \n77                   0.157895     0.558929      0.127228       0.364822   \n78                   0.973684     0.093651      0.009061       0.023889   \n80                   0.210526     0.285714      0.142952       0.035827   \n81                   0.868421     0.018783      0.007366       0.011368   \n82                   0.331523     0.111111      0.008398       0.200369   \n\n    log_litterclutch_size_n  log_birthhatching_weight_g  log_weaning_weight_g  \\\n1                  0.510529                    0.573453              0.473731   \n3                  0.009111                    0.949202              1.000000   \n4                  0.246598                    0.388389              0.291012   \n5                  0.372810                    0.409974              0.245827   \n6                  0.009111                    0.712620              0.612956   \n..                      ...                         ...                   ...   \n77                 0.011360                    0.606232              0.521577   \n78                 0.671602                    0.256778              0.066038   \n80                 0.011360                    0.161409              0.434306   \n81                 0.776263                    0.253005              0.100441   \n82                 0.009111                    0.332081              0.136332   \n\n    log_male_body_mass_g  log_range_size  X2.1_AgeatEyeOpening_d  \\\n1               0.687300        0.647623                0.237632   \n3               0.949329        0.943420                0.047526   \n4               0.325862        0.432175                0.165789   \n5               0.307691        0.495103                0.269579   \n6               0.635400        0.755294                0.000000   \n..                   ...             ...                     ...   \n77              0.618408        0.685910                0.337632   \n78              0.113736        0.659369                0.342105   \n80              0.535588        0.509135                0.092263   \n81              0.160721        0.565088                0.236842   \n82              0.148512        0.728117                0.213158   \n\n    X9.1_GestationLen_d  X10.2_SocialGrpSize  X24.1_TeatNumber  \\\n1              0.170946             0.245312              1.00   \n3              0.625145             0.015625              0.24   \n4              0.206341             0.026719              0.22   \n5              0.281881             0.117969              0.32   \n6              0.741588             0.149063              0.12   \n..                  ...                  ...               ...   \n77             0.356809             0.609375              0.28   \n78             0.022911             0.029063              0.20   \n80             0.046744             0.000000              0.16   \n81             0.017382             0.000000              0.42   \n82             0.204659             0.032969              0.20   \n\n    X6.2_TrophicLevel  X26.2_GR_MaxLat_dd  X26.3_GR_MinLat_dd  \\\n1                 1.0            0.630613            0.250419   \n3                 1.0            0.420887            0.562710   \n4                 0.6            0.437272            0.548509   \n5                 0.5            0.304684            0.447987   \n6                 0.5            0.420094            0.565519   \n..                ...                 ...                 ...   \n77                0.3            0.503219            0.496015   \n78                0.5            0.738532            0.723154   \n80                0.0            0.188073            0.146497   \n81                0.4            0.752294            0.939073   \n82                0.5            0.485112            0.315436   \n\n    X26.6_GR_MinLong_dd  X28.1_Precip_Mean_mm  X28.2_Temp_Mean_01degC  \\\n1              0.508595              0.170364                0.876430   \n3              0.600141              0.586530                0.709050   \n4              0.600141              0.590206                0.720736   \n5              0.413494              0.376488                0.936746   \n6              0.600141              0.590337                0.722186   \n..                  ...                   ...                     ...   \n77             0.516574              0.371563                0.913839   \n78             0.207147              0.226724                0.558673   \n80             0.980468              0.194818                0.711532   \n81             0.816081              0.050364                0.124550   \n82             0.255422              0.611006                0.913460   \n\n    X30.1_AET_Mean_mm  log_HomeRange_km2  log_HuPopDen_Mean_n.km2  \\\n1            0.288623           0.870614                 0.409161   \n3            0.484833           0.731489                 0.214465   \n4            0.472741           0.474696                 0.264731   \n5            0.496785           0.297757                 0.570234   \n6            0.478088           0.660577                 0.247822   \n..                ...                ...                      ...   \n77           0.567265           0.672284                 0.506840   \n78           0.356003           0.085067                 0.522265   \n80           0.331348           0.250443                 0.370954   \n81           0.109553           0.376487                 0.420074   \n82           0.752220           0.344578                 0.427522   \n\n    log_NeonateHeadBodyLen_mm  Cingulata  Didelphimorphia  Erinaceomorpha  \\\n1                    0.565604        0.0              0.0             0.0   \n3                    0.866916        0.0              0.0             0.0   \n4                    0.302667        0.0              0.0             0.0   \n5                    0.376771        0.0              0.0             0.0   \n6                    0.818692        0.0              0.0             0.0   \n..                        ...        ...              ...             ...   \n77                   0.552929        0.0              0.0             0.0   \n78                   0.203005        0.0              0.0             0.0   \n80                   0.098918        0.0              0.0             0.0   \n81                   0.191091        0.0              0.0             0.0   \n82                   0.259348        0.0              0.0             0.0   \n\n    Perissodactyla  Pholidota  Primates  Rodentia  Soricomorpha  AA_83_Y  \\\n1              0.0        0.0       0.0       0.0           0.0      1.0   \n3              0.0        0.0       0.0       0.0           0.0      1.0   \n4              0.0        0.0       0.0       0.0           0.0      1.0   \n5              0.0        0.0       1.0       0.0           0.0      1.0   \n6              0.0        0.0       0.0       0.0           0.0      1.0   \n..             ...        ...       ...       ...           ...      ...   \n77             0.0        0.0       1.0       0.0           0.0      1.0   \n78             0.0        0.0       0.0       1.0           0.0      1.0   \n80             0.0        0.0       0.0       0.0           0.0      0.0   \n81             0.0        0.0       0.0       1.0           0.0      1.0   \n82             0.0        0.0       0.0       0.0           0.0      1.0   \n\n    AA_30_negative  binary_haddock_score  \n1              1.0                   0.0  \n3              0.0                   0.0  \n4              1.0                   0.0  \n5              1.0                   1.0  \n6              1.0                   1.0  \n..             ...                   ...  \n77             1.0                   1.0  \n78             1.0                   1.0  \n80             1.0                   0.0  \n81             1.0                   1.0  \n82             1.0                   1.0  \n\n[64 rows x 41 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ForStrat_terrestrial</th>\n      <th>ForStrat_aquatic</th>\n      <th>Activity.Nocturnal</th>\n      <th>Activity.Crepuscular</th>\n      <th>Activity.Diurnal</th>\n      <th>female_maturity_d</th>\n      <th>weaning_d</th>\n      <th>litters_or_clutches_per_y</th>\n      <th>longevity_y</th>\n      <th>adult_svl_cm</th>\n      <th>development_d</th>\n      <th>log_litterclutch_size_n</th>\n      <th>log_birthhatching_weight_g</th>\n      <th>log_weaning_weight_g</th>\n      <th>log_male_body_mass_g</th>\n      <th>log_range_size</th>\n      <th>X2.1_AgeatEyeOpening_d</th>\n      <th>X9.1_GestationLen_d</th>\n      <th>X10.2_SocialGrpSize</th>\n      <th>X24.1_TeatNumber</th>\n      <th>X6.2_TrophicLevel</th>\n      <th>X26.2_GR_MaxLat_dd</th>\n      <th>X26.3_GR_MinLat_dd</th>\n      <th>X26.6_GR_MinLong_dd</th>\n      <th>X28.1_Precip_Mean_mm</th>\n      <th>X28.2_Temp_Mean_01degC</th>\n      <th>X30.1_AET_Mean_mm</th>\n      <th>log_HomeRange_km2</th>\n      <th>log_HuPopDen_Mean_n.km2</th>\n      <th>log_NeonateHeadBodyLen_mm</th>\n      <th>Cingulata</th>\n      <th>Didelphimorphia</th>\n      <th>Erinaceomorpha</th>\n      <th>Perissodactyla</th>\n      <th>Pholidota</th>\n      <th>Primates</th>\n      <th>Rodentia</th>\n      <th>Soricomorpha</th>\n      <th>AA_83_Y</th>\n      <th>AA_30_negative</th>\n      <th>binary_haddock_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.144858</td>\n      <td>0.104223</td>\n      <td>0.131579</td>\n      <td>0.269841</td>\n      <td>0.257353</td>\n      <td>0.164926</td>\n      <td>0.510529</td>\n      <td>0.573453</td>\n      <td>0.473731</td>\n      <td>0.687300</td>\n      <td>0.647623</td>\n      <td>0.237632</td>\n      <td>0.170946</td>\n      <td>0.245312</td>\n      <td>1.00</td>\n      <td>1.0</td>\n      <td>0.630613</td>\n      <td>0.250419</td>\n      <td>0.508595</td>\n      <td>0.170364</td>\n      <td>0.876430</td>\n      <td>0.288623</td>\n      <td>0.870614</td>\n      <td>0.409161</td>\n      <td>0.565604</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.706728</td>\n      <td>0.176509</td>\n      <td>0.210526</td>\n      <td>0.738095</td>\n      <td>0.650588</td>\n      <td>0.654653</td>\n      <td>0.009111</td>\n      <td>0.949202</td>\n      <td>1.000000</td>\n      <td>0.949329</td>\n      <td>0.943420</td>\n      <td>0.047526</td>\n      <td>0.625145</td>\n      <td>0.015625</td>\n      <td>0.24</td>\n      <td>1.0</td>\n      <td>0.420887</td>\n      <td>0.562710</td>\n      <td>0.600141</td>\n      <td>0.586530</td>\n      <td>0.709050</td>\n      <td>0.484833</td>\n      <td>0.731489</td>\n      <td>0.214465</td>\n      <td>0.866916</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.117562</td>\n      <td>0.084097</td>\n      <td>0.228638</td>\n      <td>0.146755</td>\n      <td>0.059387</td>\n      <td>0.233807</td>\n      <td>0.246598</td>\n      <td>0.388389</td>\n      <td>0.291012</td>\n      <td>0.325862</td>\n      <td>0.432175</td>\n      <td>0.165789</td>\n      <td>0.206341</td>\n      <td>0.026719</td>\n      <td>0.22</td>\n      <td>0.6</td>\n      <td>0.437272</td>\n      <td>0.548509</td>\n      <td>0.600141</td>\n      <td>0.590206</td>\n      <td>0.720736</td>\n      <td>0.472741</td>\n      <td>0.474696</td>\n      <td>0.264731</td>\n      <td>0.302667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.112845</td>\n      <td>0.050315</td>\n      <td>0.473684</td>\n      <td>0.226190</td>\n      <td>0.044673</td>\n      <td>0.287087</td>\n      <td>0.372810</td>\n      <td>0.409974</td>\n      <td>0.245827</td>\n      <td>0.307691</td>\n      <td>0.495103</td>\n      <td>0.269579</td>\n      <td>0.281881</td>\n      <td>0.117969</td>\n      <td>0.32</td>\n      <td>0.5</td>\n      <td>0.304684</td>\n      <td>0.447987</td>\n      <td>0.413494</td>\n      <td>0.376488</td>\n      <td>0.936746</td>\n      <td>0.496785</td>\n      <td>0.297757</td>\n      <td>0.570234</td>\n      <td>0.376771</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.371078</td>\n      <td>0.104223</td>\n      <td>0.130263</td>\n      <td>0.380952</td>\n      <td>0.336369</td>\n      <td>0.516945</td>\n      <td>0.009111</td>\n      <td>0.712620</td>\n      <td>0.612956</td>\n      <td>0.635400</td>\n      <td>0.755294</td>\n      <td>0.000000</td>\n      <td>0.741588</td>\n      <td>0.149063</td>\n      <td>0.12</td>\n      <td>0.5</td>\n      <td>0.420094</td>\n      <td>0.565519</td>\n      <td>0.600141</td>\n      <td>0.590337</td>\n      <td>0.722186</td>\n      <td>0.478088</td>\n      <td>0.660577</td>\n      <td>0.247822</td>\n      <td>0.818692</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.403143</td>\n      <td>0.382105</td>\n      <td>0.157895</td>\n      <td>0.558929</td>\n      <td>0.127228</td>\n      <td>0.364822</td>\n      <td>0.011360</td>\n      <td>0.606232</td>\n      <td>0.521577</td>\n      <td>0.618408</td>\n      <td>0.685910</td>\n      <td>0.337632</td>\n      <td>0.356809</td>\n      <td>0.609375</td>\n      <td>0.28</td>\n      <td>0.3</td>\n      <td>0.503219</td>\n      <td>0.496015</td>\n      <td>0.516574</td>\n      <td>0.371563</td>\n      <td>0.913839</td>\n      <td>0.567265</td>\n      <td>0.672284</td>\n      <td>0.506840</td>\n      <td>0.552929</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.006276</td>\n      <td>0.002791</td>\n      <td>0.973684</td>\n      <td>0.093651</td>\n      <td>0.009061</td>\n      <td>0.023889</td>\n      <td>0.671602</td>\n      <td>0.256778</td>\n      <td>0.066038</td>\n      <td>0.113736</td>\n      <td>0.659369</td>\n      <td>0.342105</td>\n      <td>0.022911</td>\n      <td>0.029063</td>\n      <td>0.20</td>\n      <td>0.5</td>\n      <td>0.738532</td>\n      <td>0.723154</td>\n      <td>0.207147</td>\n      <td>0.226724</td>\n      <td>0.558673</td>\n      <td>0.356003</td>\n      <td>0.085067</td>\n      <td>0.522265</td>\n      <td>0.203005</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.157196</td>\n      <td>0.385747</td>\n      <td>0.210526</td>\n      <td>0.285714</td>\n      <td>0.142952</td>\n      <td>0.035827</td>\n      <td>0.011360</td>\n      <td>0.161409</td>\n      <td>0.434306</td>\n      <td>0.535588</td>\n      <td>0.509135</td>\n      <td>0.092263</td>\n      <td>0.046744</td>\n      <td>0.000000</td>\n      <td>0.16</td>\n      <td>0.0</td>\n      <td>0.188073</td>\n      <td>0.146497</td>\n      <td>0.980468</td>\n      <td>0.194818</td>\n      <td>0.711532</td>\n      <td>0.331348</td>\n      <td>0.250443</td>\n      <td>0.370954</td>\n      <td>0.098918</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.018633</td>\n      <td>0.000000</td>\n      <td>0.868421</td>\n      <td>0.018783</td>\n      <td>0.007366</td>\n      <td>0.011368</td>\n      <td>0.776263</td>\n      <td>0.253005</td>\n      <td>0.100441</td>\n      <td>0.160721</td>\n      <td>0.565088</td>\n      <td>0.236842</td>\n      <td>0.017382</td>\n      <td>0.000000</td>\n      <td>0.42</td>\n      <td>0.4</td>\n      <td>0.752294</td>\n      <td>0.939073</td>\n      <td>0.816081</td>\n      <td>0.050364</td>\n      <td>0.124550</td>\n      <td>0.109553</td>\n      <td>0.376487</td>\n      <td>0.420074</td>\n      <td>0.191091</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.121748</td>\n      <td>0.019205</td>\n      <td>0.331523</td>\n      <td>0.111111</td>\n      <td>0.008398</td>\n      <td>0.200369</td>\n      <td>0.009111</td>\n      <td>0.332081</td>\n      <td>0.136332</td>\n      <td>0.148512</td>\n      <td>0.728117</td>\n      <td>0.213158</td>\n      <td>0.204659</td>\n      <td>0.032969</td>\n      <td>0.20</td>\n      <td>0.5</td>\n      <td>0.485112</td>\n      <td>0.315436</td>\n      <td>0.255422</td>\n      <td>0.611006</td>\n      <td>0.913460</td>\n      <td>0.752220</td>\n      <td>0.344578</td>\n      <td>0.427522</td>\n      <td>0.259348</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>64 rows × 41 columns</p>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_split 54 instances, test_split 10/15%\n",
    "# Instance selection\n",
    "chosen_instances   = [1, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 22, 24, 26, 27, 28, 29, 30, 31, \n",
    "                      32, 34, 36, 38, 39, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, \n",
    "                      62, 63, 64, 65, 66, 67, 68, 70, 71, 73, 74, 77, 78, 80, 81, 82]\n",
    "df_tmp = data\n",
    "new_data = data.iloc[chosen_instances, :]\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:51.140961841Z",
     "start_time": "2023-10-05T18:31:50.691173457Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "    ForStrat_terrestrial  ForStrat_aquatic  Activity.Nocturnal  \\\n1                    1.0               0.0                 0.0   \n3                    0.0               1.0                 0.0   \n4                    1.0               0.0                 1.0   \n5                    1.0               0.0                 0.0   \n6                    0.0               1.0                 1.0   \n..                   ...               ...                 ...   \n77                   1.0               0.0                 0.0   \n78                   1.0               0.0                 1.0   \n80                   1.0               0.0                 1.0   \n81                   1.0               0.0                 1.0   \n82                   1.0               0.0                 1.0   \n\n    Activity.Crepuscular  Activity.Diurnal  female_maturity_d  weaning_d  \\\n1                    0.0               1.0           0.144858   0.104223   \n3                    0.0               1.0           0.706728   0.176509   \n4                    0.0               0.0           0.117562   0.084097   \n5                    0.0               1.0           0.112845   0.050315   \n6                    1.0               0.0           0.371078   0.104223   \n..                   ...               ...                ...        ...   \n77                   0.0               1.0           0.403143   0.382105   \n78                   0.0               0.0           0.006276   0.002791   \n80                   0.0               0.0           0.157196   0.385747   \n81                   1.0               0.0           0.018633   0.000000   \n82                   0.0               0.0           0.121748   0.019205   \n\n    litters_or_clutches_per_y  longevity_y  adult_svl_cm  development_d  \\\n1                    0.131579     0.269841      0.257353       0.164926   \n3                    0.210526     0.738095      0.650588       0.654653   \n4                    0.228638     0.146755      0.059387       0.233807   \n5                    0.473684     0.226190      0.044673       0.287087   \n6                    0.130263     0.380952      0.336369       0.516945   \n..                        ...          ...           ...            ...   \n77                   0.157895     0.558929      0.127228       0.364822   \n78                   0.973684     0.093651      0.009061       0.023889   \n80                   0.210526     0.285714      0.142952       0.035827   \n81                   0.868421     0.018783      0.007366       0.011368   \n82                   0.331523     0.111111      0.008398       0.200369   \n\n    log_litterclutch_size_n  log_birthhatching_weight_g  log_weaning_weight_g  \\\n1                  0.510529                    0.573453              0.473731   \n3                  0.009111                    0.949202              1.000000   \n4                  0.246598                    0.388389              0.291012   \n5                  0.372810                    0.409974              0.245827   \n6                  0.009111                    0.712620              0.612956   \n..                      ...                         ...                   ...   \n77                 0.011360                    0.606232              0.521577   \n78                 0.671602                    0.256778              0.066038   \n80                 0.011360                    0.161409              0.434306   \n81                 0.776263                    0.253005              0.100441   \n82                 0.009111                    0.332081              0.136332   \n\n    log_male_body_mass_g  log_range_size  X2.1_AgeatEyeOpening_d  \\\n1               0.687300        0.647623                0.237632   \n3               0.949329        0.943420                0.047526   \n4               0.325862        0.432175                0.165789   \n5               0.307691        0.495103                0.269579   \n6               0.635400        0.755294                0.000000   \n..                   ...             ...                     ...   \n77              0.618408        0.685910                0.337632   \n78              0.113736        0.659369                0.342105   \n80              0.535588        0.509135                0.092263   \n81              0.160721        0.565088                0.236842   \n82              0.148512        0.728117                0.213158   \n\n    X9.1_GestationLen_d  X10.2_SocialGrpSize  X24.1_TeatNumber  \\\n1              0.170946             0.245312              1.00   \n3              0.625145             0.015625              0.24   \n4              0.206341             0.026719              0.22   \n5              0.281881             0.117969              0.32   \n6              0.741588             0.149063              0.12   \n..                  ...                  ...               ...   \n77             0.356809             0.609375              0.28   \n78             0.022911             0.029063              0.20   \n80             0.046744             0.000000              0.16   \n81             0.017382             0.000000              0.42   \n82             0.204659             0.032969              0.20   \n\n    X6.2_TrophicLevel  X26.2_GR_MaxLat_dd  X26.3_GR_MinLat_dd  \\\n1                 1.0            0.630613            0.250419   \n3                 1.0            0.420887            0.562710   \n4                 0.6            0.437272            0.548509   \n5                 0.5            0.304684            0.447987   \n6                 0.5            0.420094            0.565519   \n..                ...                 ...                 ...   \n77                0.3            0.503219            0.496015   \n78                0.5            0.738532            0.723154   \n80                0.0            0.188073            0.146497   \n81                0.4            0.752294            0.939073   \n82                0.5            0.485112            0.315436   \n\n    X26.6_GR_MinLong_dd  X28.1_Precip_Mean_mm  X28.2_Temp_Mean_01degC  \\\n1              0.508595              0.170364                0.876430   \n3              0.600141              0.586530                0.709050   \n4              0.600141              0.590206                0.720736   \n5              0.413494              0.376488                0.936746   \n6              0.600141              0.590337                0.722186   \n..                  ...                   ...                     ...   \n77             0.516574              0.371563                0.913839   \n78             0.207147              0.226724                0.558673   \n80             0.980468              0.194818                0.711532   \n81             0.816081              0.050364                0.124550   \n82             0.255422              0.611006                0.913460   \n\n    X30.1_AET_Mean_mm  log_HomeRange_km2  log_HuPopDen_Mean_n.km2  \\\n1            0.288623           0.870614                 0.409161   \n3            0.484833           0.731489                 0.214465   \n4            0.472741           0.474696                 0.264731   \n5            0.496785           0.297757                 0.570234   \n6            0.478088           0.660577                 0.247822   \n..                ...                ...                      ...   \n77           0.567265           0.672284                 0.506840   \n78           0.356003           0.085067                 0.522265   \n80           0.331348           0.250443                 0.370954   \n81           0.109553           0.376487                 0.420074   \n82           0.752220           0.344578                 0.427522   \n\n    log_NeonateHeadBodyLen_mm  Cingulata  Didelphimorphia  Erinaceomorpha  \\\n1                    0.565604        0.0              0.0             0.0   \n3                    0.866916        0.0              0.0             0.0   \n4                    0.302667        0.0              0.0             0.0   \n5                    0.376771        0.0              0.0             0.0   \n6                    0.818692        0.0              0.0             0.0   \n..                        ...        ...              ...             ...   \n77                   0.552929        0.0              0.0             0.0   \n78                   0.203005        0.0              0.0             0.0   \n80                   0.098918        0.0              0.0             0.0   \n81                   0.191091        0.0              0.0             0.0   \n82                   0.259348        0.0              0.0             0.0   \n\n    Perissodactyla  Pholidota  Primates  Rodentia  Soricomorpha  AA_83_Y  \\\n1              0.0        0.0       0.0       0.0           0.0      1.0   \n3              0.0        0.0       0.0       0.0           0.0      1.0   \n4              0.0        0.0       0.0       0.0           0.0      1.0   \n5              0.0        0.0       1.0       0.0           0.0      1.0   \n6              0.0        0.0       0.0       0.0           0.0      1.0   \n..             ...        ...       ...       ...           ...      ...   \n77             0.0        0.0       1.0       0.0           0.0      1.0   \n78             0.0        0.0       0.0       1.0           0.0      1.0   \n80             0.0        0.0       0.0       0.0           0.0      0.0   \n81             0.0        0.0       0.0       1.0           0.0      1.0   \n82             0.0        0.0       0.0       0.0           0.0      1.0   \n\n    AA_30_negative  \n1              1.0  \n3              0.0  \n4              1.0  \n5              1.0  \n6              1.0  \n..             ...  \n77             1.0  \n78             1.0  \n80             1.0  \n81             1.0  \n82             1.0  \n\n[64 rows x 40 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ForStrat_terrestrial</th>\n      <th>ForStrat_aquatic</th>\n      <th>Activity.Nocturnal</th>\n      <th>Activity.Crepuscular</th>\n      <th>Activity.Diurnal</th>\n      <th>female_maturity_d</th>\n      <th>weaning_d</th>\n      <th>litters_or_clutches_per_y</th>\n      <th>longevity_y</th>\n      <th>adult_svl_cm</th>\n      <th>development_d</th>\n      <th>log_litterclutch_size_n</th>\n      <th>log_birthhatching_weight_g</th>\n      <th>log_weaning_weight_g</th>\n      <th>log_male_body_mass_g</th>\n      <th>log_range_size</th>\n      <th>X2.1_AgeatEyeOpening_d</th>\n      <th>X9.1_GestationLen_d</th>\n      <th>X10.2_SocialGrpSize</th>\n      <th>X24.1_TeatNumber</th>\n      <th>X6.2_TrophicLevel</th>\n      <th>X26.2_GR_MaxLat_dd</th>\n      <th>X26.3_GR_MinLat_dd</th>\n      <th>X26.6_GR_MinLong_dd</th>\n      <th>X28.1_Precip_Mean_mm</th>\n      <th>X28.2_Temp_Mean_01degC</th>\n      <th>X30.1_AET_Mean_mm</th>\n      <th>log_HomeRange_km2</th>\n      <th>log_HuPopDen_Mean_n.km2</th>\n      <th>log_NeonateHeadBodyLen_mm</th>\n      <th>Cingulata</th>\n      <th>Didelphimorphia</th>\n      <th>Erinaceomorpha</th>\n      <th>Perissodactyla</th>\n      <th>Pholidota</th>\n      <th>Primates</th>\n      <th>Rodentia</th>\n      <th>Soricomorpha</th>\n      <th>AA_83_Y</th>\n      <th>AA_30_negative</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.144858</td>\n      <td>0.104223</td>\n      <td>0.131579</td>\n      <td>0.269841</td>\n      <td>0.257353</td>\n      <td>0.164926</td>\n      <td>0.510529</td>\n      <td>0.573453</td>\n      <td>0.473731</td>\n      <td>0.687300</td>\n      <td>0.647623</td>\n      <td>0.237632</td>\n      <td>0.170946</td>\n      <td>0.245312</td>\n      <td>1.00</td>\n      <td>1.0</td>\n      <td>0.630613</td>\n      <td>0.250419</td>\n      <td>0.508595</td>\n      <td>0.170364</td>\n      <td>0.876430</td>\n      <td>0.288623</td>\n      <td>0.870614</td>\n      <td>0.409161</td>\n      <td>0.565604</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.706728</td>\n      <td>0.176509</td>\n      <td>0.210526</td>\n      <td>0.738095</td>\n      <td>0.650588</td>\n      <td>0.654653</td>\n      <td>0.009111</td>\n      <td>0.949202</td>\n      <td>1.000000</td>\n      <td>0.949329</td>\n      <td>0.943420</td>\n      <td>0.047526</td>\n      <td>0.625145</td>\n      <td>0.015625</td>\n      <td>0.24</td>\n      <td>1.0</td>\n      <td>0.420887</td>\n      <td>0.562710</td>\n      <td>0.600141</td>\n      <td>0.586530</td>\n      <td>0.709050</td>\n      <td>0.484833</td>\n      <td>0.731489</td>\n      <td>0.214465</td>\n      <td>0.866916</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.117562</td>\n      <td>0.084097</td>\n      <td>0.228638</td>\n      <td>0.146755</td>\n      <td>0.059387</td>\n      <td>0.233807</td>\n      <td>0.246598</td>\n      <td>0.388389</td>\n      <td>0.291012</td>\n      <td>0.325862</td>\n      <td>0.432175</td>\n      <td>0.165789</td>\n      <td>0.206341</td>\n      <td>0.026719</td>\n      <td>0.22</td>\n      <td>0.6</td>\n      <td>0.437272</td>\n      <td>0.548509</td>\n      <td>0.600141</td>\n      <td>0.590206</td>\n      <td>0.720736</td>\n      <td>0.472741</td>\n      <td>0.474696</td>\n      <td>0.264731</td>\n      <td>0.302667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.112845</td>\n      <td>0.050315</td>\n      <td>0.473684</td>\n      <td>0.226190</td>\n      <td>0.044673</td>\n      <td>0.287087</td>\n      <td>0.372810</td>\n      <td>0.409974</td>\n      <td>0.245827</td>\n      <td>0.307691</td>\n      <td>0.495103</td>\n      <td>0.269579</td>\n      <td>0.281881</td>\n      <td>0.117969</td>\n      <td>0.32</td>\n      <td>0.5</td>\n      <td>0.304684</td>\n      <td>0.447987</td>\n      <td>0.413494</td>\n      <td>0.376488</td>\n      <td>0.936746</td>\n      <td>0.496785</td>\n      <td>0.297757</td>\n      <td>0.570234</td>\n      <td>0.376771</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.371078</td>\n      <td>0.104223</td>\n      <td>0.130263</td>\n      <td>0.380952</td>\n      <td>0.336369</td>\n      <td>0.516945</td>\n      <td>0.009111</td>\n      <td>0.712620</td>\n      <td>0.612956</td>\n      <td>0.635400</td>\n      <td>0.755294</td>\n      <td>0.000000</td>\n      <td>0.741588</td>\n      <td>0.149063</td>\n      <td>0.12</td>\n      <td>0.5</td>\n      <td>0.420094</td>\n      <td>0.565519</td>\n      <td>0.600141</td>\n      <td>0.590337</td>\n      <td>0.722186</td>\n      <td>0.478088</td>\n      <td>0.660577</td>\n      <td>0.247822</td>\n      <td>0.818692</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.403143</td>\n      <td>0.382105</td>\n      <td>0.157895</td>\n      <td>0.558929</td>\n      <td>0.127228</td>\n      <td>0.364822</td>\n      <td>0.011360</td>\n      <td>0.606232</td>\n      <td>0.521577</td>\n      <td>0.618408</td>\n      <td>0.685910</td>\n      <td>0.337632</td>\n      <td>0.356809</td>\n      <td>0.609375</td>\n      <td>0.28</td>\n      <td>0.3</td>\n      <td>0.503219</td>\n      <td>0.496015</td>\n      <td>0.516574</td>\n      <td>0.371563</td>\n      <td>0.913839</td>\n      <td>0.567265</td>\n      <td>0.672284</td>\n      <td>0.506840</td>\n      <td>0.552929</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.006276</td>\n      <td>0.002791</td>\n      <td>0.973684</td>\n      <td>0.093651</td>\n      <td>0.009061</td>\n      <td>0.023889</td>\n      <td>0.671602</td>\n      <td>0.256778</td>\n      <td>0.066038</td>\n      <td>0.113736</td>\n      <td>0.659369</td>\n      <td>0.342105</td>\n      <td>0.022911</td>\n      <td>0.029063</td>\n      <td>0.20</td>\n      <td>0.5</td>\n      <td>0.738532</td>\n      <td>0.723154</td>\n      <td>0.207147</td>\n      <td>0.226724</td>\n      <td>0.558673</td>\n      <td>0.356003</td>\n      <td>0.085067</td>\n      <td>0.522265</td>\n      <td>0.203005</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.157196</td>\n      <td>0.385747</td>\n      <td>0.210526</td>\n      <td>0.285714</td>\n      <td>0.142952</td>\n      <td>0.035827</td>\n      <td>0.011360</td>\n      <td>0.161409</td>\n      <td>0.434306</td>\n      <td>0.535588</td>\n      <td>0.509135</td>\n      <td>0.092263</td>\n      <td>0.046744</td>\n      <td>0.000000</td>\n      <td>0.16</td>\n      <td>0.0</td>\n      <td>0.188073</td>\n      <td>0.146497</td>\n      <td>0.980468</td>\n      <td>0.194818</td>\n      <td>0.711532</td>\n      <td>0.331348</td>\n      <td>0.250443</td>\n      <td>0.370954</td>\n      <td>0.098918</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.018633</td>\n      <td>0.000000</td>\n      <td>0.868421</td>\n      <td>0.018783</td>\n      <td>0.007366</td>\n      <td>0.011368</td>\n      <td>0.776263</td>\n      <td>0.253005</td>\n      <td>0.100441</td>\n      <td>0.160721</td>\n      <td>0.565088</td>\n      <td>0.236842</td>\n      <td>0.017382</td>\n      <td>0.000000</td>\n      <td>0.42</td>\n      <td>0.4</td>\n      <td>0.752294</td>\n      <td>0.939073</td>\n      <td>0.816081</td>\n      <td>0.050364</td>\n      <td>0.124550</td>\n      <td>0.109553</td>\n      <td>0.376487</td>\n      <td>0.420074</td>\n      <td>0.191091</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.121748</td>\n      <td>0.019205</td>\n      <td>0.331523</td>\n      <td>0.111111</td>\n      <td>0.008398</td>\n      <td>0.200369</td>\n      <td>0.009111</td>\n      <td>0.332081</td>\n      <td>0.136332</td>\n      <td>0.148512</td>\n      <td>0.728117</td>\n      <td>0.213158</td>\n      <td>0.204659</td>\n      <td>0.032969</td>\n      <td>0.20</td>\n      <td>0.5</td>\n      <td>0.485112</td>\n      <td>0.315436</td>\n      <td>0.255422</td>\n      <td>0.611006</td>\n      <td>0.913460</td>\n      <td>0.752220</td>\n      <td>0.344578</td>\n      <td>0.427522</td>\n      <td>0.259348</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>64 rows × 40 columns</p>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = new_data['binary_haddock_score']\n",
    "x = new_data.drop(['binary_haddock_score'], axis=1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:51.163709457Z",
     "start_time": "2023-10-05T18:31:50.734972937Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "((57, 40), (57,), (7, 40), (7,))"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, stratify=y, test_size=0.10, random_state=42)\n",
    "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:51.331960801Z",
     "start_time": "2023-10-05T18:31:50.742116360Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "((57, 40), (57,), (56, 40), (56,))"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = x_test._append(df_tmp.iloc[~df_tmp.index.isin(chosen_instances), :-1])\n",
    "y_test = y_test._append(df_tmp.iloc[~df_tmp.index.isin(chosen_instances), -1])\n",
    "\n",
    "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:51.333474570Z",
     "start_time": "2023-10-05T18:31:50.750814053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "binary_haddock_score\n0.0    29\n1.0    28\nName: count, dtype: int64"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:51.334507790Z",
     "start_time": "2023-10-05T18:31:50.785136567Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "kf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42) # random_state=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:31:51.335510764Z",
     "start_time": "2023-10-05T18:31:50.785758227Z"
    }
   },
   "outputs": [],
   "source": [
    "import copy\n",
    "import sklearn.metrics as metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "\n",
    "def perform_cross_validation(model, x, y, num_folds=10):\n",
    "    kf = StratifiedKFold(n_splits=num_folds, shuffle=True, random_state=42) #random_state=2\n",
    "    melhor = 0\n",
    "    fold = 1\n",
    "    labels = ['Não suceptível', 'Suceptível']\n",
    "    labels2 = [0, 1]\n",
    "\n",
    "    precision_valid = [0, 0]\n",
    "    recall_valid = [0, 0]\n",
    "    fscore_valid = [0, 0]\n",
    "\n",
    "    best_model = None\n",
    "\n",
    "    array_precision = []\n",
    "\n",
    "    for train_index, valid_index in kf.split(x, y):\n",
    "        x_train, x_valid = x.iloc[train_index], x.iloc[valid_index]\n",
    "        y_train, y_valid = y.iloc[train_index], y.iloc[valid_index]\n",
    "\n",
    "        model.fit(x_train, y_train)\n",
    "        previsoes = model.predict(x_valid)\n",
    "\n",
    "        # Calculating metrics\n",
    "        precision, recall, fscore, support = precision_recall_fscore_support(y_valid, previsoes, average=None)\n",
    "\n",
    "        precision_valid = np.add(precision_valid, precision)\n",
    "        recall_valid = np.add(recall_valid, recall)\n",
    "        fscore_valid = np.add(fscore_valid, fscore)\n",
    "\n",
    "        accuracy = np.mean(y_valid == previsoes)\n",
    "\n",
    "        if accuracy > melhor:\n",
    "            melhor = accuracy\n",
    "            best_model = copy.deepcopy(model)\n",
    "\n",
    "        array_precision.append(accuracy)\n",
    "\n",
    "        print('FOLD:', fold)\n",
    "        print(metrics.classification_report(y_valid, previsoes, target_names=labels))\n",
    "\n",
    "        cm = metrics.confusion_matrix(y_valid, previsoes, labels=labels2)\n",
    "        disp = metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels2)\n",
    "        disp.plot()\n",
    "        plt.show()\n",
    "\n",
    "        fold += 1\n",
    "        print('---------------------------------------------------------')\n",
    "\n",
    "    #plt.show(best_model)\n",
    "    print(\"Precisão média na validação das classes Inferior e Superior:\", np.round(precision_valid/num_folds, 2))\n",
    "    print(\"----------------------------------------------------------------\")\n",
    "    print(\"Recall médio na validação das classes Inferior e Superior:\", np.round(recall_valid/num_folds, 2))\n",
    "    print(\"----------------------------------------------------------------\")\n",
    "    print(\"F1 score médio na validação das classes Inferior e Superior:\", np.round(fscore_valid/num_folds, 2))\n",
    "    print(\"----------------------------------------------------------------\")\n",
    "    \n",
    "    \n",
    "    return best_model, array_precision\n",
    "\n",
    "#def plot_crossval(model):\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Melhor Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:38:01.032807097Z",
     "start_time": "2023-10-05T18:37:54.612637604Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOLD: 1\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "Não suceptível       0.75      1.00      0.86         3\n",
      "    Suceptível       1.00      0.67      0.80         3\n",
      "\n",
      "      accuracy                           0.83         6\n",
      "     macro avg       0.88      0.83      0.83         6\n",
      "  weighted avg       0.88      0.83      0.83         6\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------\n",
      "FOLD: 2\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "Não suceptível       1.00      0.67      0.80         3\n",
      "    Suceptível       0.75      1.00      0.86         3\n",
      "\n",
      "      accuracy                           0.83         6\n",
      "     macro avg       0.88      0.83      0.83         6\n",
      "  weighted avg       0.88      0.83      0.83         6\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------\n",
      "FOLD: 3\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "Não suceptível       0.67      0.67      0.67         3\n",
      "    Suceptível       0.67      0.67      0.67         3\n",
      "\n",
      "      accuracy                           0.67         6\n",
      "     macro avg       0.67      0.67      0.67         6\n",
      "  weighted avg       0.67      0.67      0.67         6\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------\n",
      "FOLD: 4\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "Não suceptível       0.75      1.00      0.86         3\n",
      "    Suceptível       1.00      0.67      0.80         3\n",
      "\n",
      "      accuracy                           0.83         6\n",
      "     macro avg       0.88      0.83      0.83         6\n",
      "  weighted avg       0.88      0.83      0.83         6\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------\n",
      "FOLD: 5\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "Não suceptível       0.75      1.00      0.86         3\n",
      "    Suceptível       1.00      0.67      0.80         3\n",
      "\n",
      "      accuracy                           0.83         6\n",
      "     macro avg       0.88      0.83      0.83         6\n",
      "  weighted avg       0.88      0.83      0.83         6\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------\n",
      "FOLD: 6\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "Não suceptível       1.00      1.00      1.00         3\n",
      "    Suceptível       1.00      1.00      1.00         3\n",
      "\n",
      "      accuracy                           1.00         6\n",
      "     macro avg       1.00      1.00      1.00         6\n",
      "  weighted avg       1.00      1.00      1.00         6\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------\n",
      "FOLD: 7\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "Não suceptível       0.75      1.00      0.86         3\n",
      "    Suceptível       1.00      0.67      0.80         3\n",
      "\n",
      "      accuracy                           0.83         6\n",
      "     macro avg       0.88      0.83      0.83         6\n",
      "  weighted avg       0.88      0.83      0.83         6\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------\n",
      "FOLD: 8\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "Não suceptível       1.00      1.00      1.00         2\n",
      "    Suceptível       1.00      1.00      1.00         3\n",
      "\n",
      "      accuracy                           1.00         5\n",
      "     macro avg       1.00      1.00      1.00         5\n",
      "  weighted avg       1.00      1.00      1.00         5\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------\n",
      "FOLD: 9\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "Não suceptível       1.00      1.00      1.00         3\n",
      "    Suceptível       1.00      1.00      1.00         2\n",
      "\n",
      "      accuracy                           1.00         5\n",
      "     macro avg       1.00      1.00      1.00         5\n",
      "  weighted avg       1.00      1.00      1.00         5\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------\n",
      "FOLD: 10\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "Não suceptível       1.00      0.33      0.50         3\n",
      "    Suceptível       0.50      1.00      0.67         2\n",
      "\n",
      "      accuracy                           0.60         5\n",
      "     macro avg       0.75      0.67      0.58         5\n",
      "  weighted avg       0.80      0.60      0.57         5\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------\n",
      "Precisão média na validação das classes Inferior e Superior: [0.87 0.89]\n",
      "----------------------------------------------------------------\n",
      "Recall médio na validação das classes Inferior e Superior: [0.87 0.83]\n",
      "----------------------------------------------------------------\n",
      "F1 score médio na validação das classes Inferior e Superior: [0.84 0.84]\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(15,15),\n",
    "                    alpha=0.0001,\n",
    "                    solver='adam',\n",
    "                    learning_rate='constant',\n",
    "                    batch_size=32,\n",
    "                    random_state=42,\n",
    "                    seed=1,\n",
    "                    activation='relu',\n",
    "                    max_iter=6000)\n",
    "\n",
    "mlpBest, mlp_array_precision = perform_cross_validation(mlp, x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-05T18:38:05.834069635Z",
     "start_time": "2023-10-05T18:38:05.786021721Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.87      0.77      0.82        26\n",
      "         1.0       0.82      0.90      0.86        30\n",
      "\n",
      "    accuracy                           0.84        56\n",
      "   macro avg       0.84      0.83      0.84        56\n",
      "weighted avg       0.84      0.84      0.84        56\n",
      "\n",
      "Accuracy: 83.92857142857143\n"
     ]
    }
   ],
   "source": [
    "mlp_accuracy = accuracy_score(y_test, mlpBest.predict(x_test))*100\n",
    "print(\"MLP\", classification_report(y_test, mlpBest.predict(x_test)))\n",
    "print('Accuracy:', mlp_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}