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TiMBL 6.4.14 (c) CLST/ILK/CLIPS 1998 - 2019.
Tilburg Memory Based Learner
Centre for Language and Speech Technology, Radboud University
Induction of Linguistic Knowledge Research Group, Tilburg University
CLiPS Computational Linguistics Group, University of Antwerp
Mon Sep 2 15:22:38 2019
Starting Cross validation test on files:
./tests/klein_1.train
./tests/klein_2.train
./tests/klein_3.train
./tests/klein_4.train
./tests/klein_5.train
Examine datafile './tests/klein_1.train' gave the following results:
Number of Features: 8
InputFormat : C4.5
DB Entropy : 1.1239577
Number of Classes : 3
Feats Vals InfoGain GainRatio
1 31 1.0514606 0.21636590
2 31 1.1239577 0.23128409
3 29 0.96606292 0.20546896
4 30 0.76843726 0.16052103
5 17 0.75290857 0.19555822
6 17 1.1239577 0.29193340
7 18 0.86079976 0.21819805
8 17 0.40001621 0.10329552
Starting to test, Testfile: ./tests/klein_1.train
Writing output in: ./tests/klein_1.train.cv
Algorithm : CV
Global metric : Overlap
Deviant Feature Metrics:(none)
Weighting : GainRatio
Feature 1 : 0.216365903867902
Feature 2 : 0.231284092171772
Feature 3 : 0.205468961117581
Feature 4 : 0.160521026415060
Feature 5 : 0.195558220224054
Feature 6 : 0.291933402045204
Feature 7 : 0.218198052865363
Feature 8 : 0.103295521355847
Tested: 1 @ Mon Sep 2 15:22:38 2019
Tested: 2 @ Mon Sep 2 15:22:38 2019
Tested: 3 @ Mon Sep 2 15:22:38 2019
Tested: 4 @ Mon Sep 2 15:22:38 2019
Tested: 5 @ Mon Sep 2 15:22:38 2019
Tested: 6 @ Mon Sep 2 15:22:38 2019
Tested: 7 @ Mon Sep 2 15:22:38 2019
Tested: 8 @ Mon Sep 2 15:22:38 2019
Tested: 9 @ Mon Sep 2 15:22:38 2019
Tested: 10 @ Mon Sep 2 15:22:38 2019
Ready: 10 @ Mon Sep 2 15:22:38 2019
Seconds taken: 0.0002 (43290.04 p/s)
Scores per Value Class:
class | TP FP TN FN precision recall(TPR) FPR F-score AUC
I | 5 0 3 2 1.00000 0.71429 0.00000 0.83333 0.85714
O | 2 2 6 0 0.50000 1.00000 0.25000 0.66667 0.87500
B | 1 0 9 0 1.00000 1.00000 0.00000 1.00000 1.00000
F-Score beta=1, microav: 0.816667
F-Score beta=1, macroav: 0.833333
AUC, microav: 0.875000
AUC, macroav: 0.910714
overall accuracy: 0.800000 (8/10)
Examine datafile './tests/klein_2.train' gave the following results:
Number of Features: 8
InputFormat : C4.5
DB Entropy : 1.21081003
Number of Classes : 3
Feats Vals InfoGain GainRatio
1 30 1.08568141 0.22585381
2 29 1.13831299 0.24042877
3 25 1.01318437 0.23032898
4 27 0.93041471 0.20656967
5 15 0.75002647 0.20299617
6 15 1.13831299 0.30537853
7 16 0.83542417 0.22216874
8 15 0.44603650 0.12394414
Starting to test, Testfile: ./tests/klein_2.train
Writing output in: ./tests/klein_2.train.cv
Algorithm : CV
Global metric : Overlap
Deviant Feature Metrics:(none)
Weighting : GainRatio
Feature 1 : 0.225853813146603
Feature 2 : 0.240428769594743
Feature 3 : 0.230328984331396
Feature 4 : 0.206569670528383
Feature 5 : 0.202996166718337
Feature 6 : 0.305378526354465
Feature 7 : 0.222168743865479
Feature 8 : 0.123944137808201
Tested: 1 @ Mon Sep 2 15:22:38 2019
Tested: 2 @ Mon Sep 2 15:22:38 2019
Tested: 3 @ Mon Sep 2 15:22:38 2019
Tested: 4 @ Mon Sep 2 15:22:38 2019
Tested: 5 @ Mon Sep 2 15:22:38 2019
Tested: 6 @ Mon Sep 2 15:22:38 2019
Tested: 7 @ Mon Sep 2 15:22:38 2019
Tested: 8 @ Mon Sep 2 15:22:38 2019
Tested: 9 @ Mon Sep 2 15:22:38 2019
Tested: 10 @ Mon Sep 2 15:22:38 2019
Ready: 10 @ Mon Sep 2 15:22:38 2019
Seconds taken: 0.0002 (47619.05 p/s)
Scores per Value Class:
class | TP FP TN FN precision recall(TPR) FPR F-score AUC
I | 5 0 3 2 1.00000 0.71429 0.00000 0.83333 0.85714
O | 3 1 6 0 0.75000 1.00000 0.14286 0.85714 0.92857
B | 0 1 9 0 0.00000 (nan) 0.10000 (nan) (nan)
F-Score beta=1, microav: 0.840476
F-Score beta=1, macroav: 0.845238
AUC, microav: 0.878571
AUC, macroav: 0.892857
overall accuracy: 0.800000 (8/10)
Examine datafile './tests/klein_3.train' gave the following results:
Number of Features: 8
InputFormat : C4.5
DB Entropy : 1.10727241
Number of Classes : 3
Feats Vals InfoGain GainRatio
1 33 1.05464083 0.21157263
2 31 1.05464083 0.21613678
3 29 0.96924313 0.20527803
4 29 0.80175556 0.17134265
5 17 0.73188656 0.19159785
6 17 1.05464083 0.27609049
7 17 0.76788828 0.20441129
8 16 0.30124108 0.08102333
Starting to test, Testfile: ./tests/klein_3.train
Writing output in: ./tests/klein_3.train.cv
Algorithm : CV
Global metric : Overlap
Deviant Feature Metrics:(none)
Weighting : GainRatio
Feature 1 : 0.211572632756454
Feature 2 : 0.216136783583368
Feature 3 : 0.205278026077873
Feature 4 : 0.171342647360174
Feature 5 : 0.191597849237135
Feature 6 : 0.276090486447543
Feature 7 : 0.204411289106452
Feature 8 : 0.081023332351012
Tested: 1 @ Mon Sep 2 15:22:38 2019
Tested: 2 @ Mon Sep 2 15:22:38 2019
Tested: 3 @ Mon Sep 2 15:22:38 2019
Tested: 4 @ Mon Sep 2 15:22:38 2019
Tested: 5 @ Mon Sep 2 15:22:38 2019
Tested: 6 @ Mon Sep 2 15:22:38 2019
Tested: 7 @ Mon Sep 2 15:22:38 2019
Tested: 8 @ Mon Sep 2 15:22:38 2019
Tested: 9 @ Mon Sep 2 15:22:38 2019
Tested: 10 @ Mon Sep 2 15:22:38 2019
Ready: 10 @ Mon Sep 2 15:22:38 2019
Seconds taken: 0.0002 (50761.42 p/s)
Scores per Value Class:
class | TP FP TN FN precision recall(TPR) FPR F-score AUC
I | 6 1 3 0 0.85714 1.00000 0.25000 0.92308 0.87500
O | 2 0 7 1 1.00000 0.66667 0.00000 0.80000 0.83333
B | 1 0 9 0 1.00000 1.00000 0.00000 1.00000 1.00000
F-Score beta=1, microav: 0.893846
F-Score beta=1, macroav: 0.907692
AUC, microav: 0.875000
AUC, macroav: 0.902778
overall accuracy: 0.900000 (9/10)
Examine datafile './tests/klein_4.train' gave the following results:
Number of Features: 8
InputFormat : C4.5
DB Entropy : 1.16743672
Number of Classes : 3
Feats Vals InfoGain GainRatio
1 32 1.11480514 0.22602878
2 32 1.09493968 0.22289880
3 30 0.96981107 0.20714134
4 31 1.06217357 0.22200231
5 16 0.71361797 0.19418620
6 16 1.09493968 0.29899921
7 17 0.76845621 0.20564998
8 15 0.53212858 0.14797537
Starting to test, Testfile: ./tests/klein_4.train
Writing output in: ./tests/klein_4.train.cv
Algorithm : CV
Global metric : Overlap
Deviant Feature Metrics:(none)
Weighting : GainRatio
Feature 1 : 0.226028780085344
Feature 2 : 0.222898804190488
Feature 3 : 0.207141340441089
Feature 4 : 0.222002305500848
Feature 5 : 0.194186199995710
Feature 6 : 0.298999207112076
Feature 7 : 0.205649984076972
Feature 8 : 0.147975374298064
Tested: 1 @ Mon Sep 2 15:22:38 2019
Tested: 2 @ Mon Sep 2 15:22:38 2019
Tested: 3 @ Mon Sep 2 15:22:38 2019
Tested: 4 @ Mon Sep 2 15:22:38 2019
Tested: 5 @ Mon Sep 2 15:22:38 2019
Tested: 6 @ Mon Sep 2 15:22:38 2019
Tested: 7 @ Mon Sep 2 15:22:38 2019
Tested: 8 @ Mon Sep 2 15:22:38 2019
Tested: 9 @ Mon Sep 2 15:22:38 2019
Tested: 10 @ Mon Sep 2 15:22:38 2019
Ready: 10 @ Mon Sep 2 15:22:38 2019
Seconds taken: 0.0002 (45045.05 p/s)
Scores per Value Class:
class | TP FP TN FN precision recall(TPR) FPR F-score AUC
I | 5 2 3 0 0.71429 1.00000 0.40000 0.83333 0.80000
O | 3 0 5 2 1.00000 0.60000 0.00000 0.75000 0.80000
B | 0 0 10 0 (nan) (nan) 0.00000 (nan) (nan)
F-Score beta=1, microav: 0.791667
F-Score beta=1, macroav: 0.791667
AUC, microav: 0.800000
AUC, macroav: 0.800000
overall accuracy: 0.800000 (8/10)
Examine datafile './tests/klein_5.train' gave the following results:
Number of Features: 8
InputFormat : C4.5
DB Entropy : 1.16687507
Number of Classes : 3
Feats Vals InfoGain GainRatio
1 32 1.06687507 0.21675958
2 31 1.11687507 0.22924703
3 29 0.98574726 0.20875948
4 28 0.82663406 0.17847364
5 16 0.69800288 0.18631101
6 16 1.11687507 0.29662222
7 16 0.78574726 0.21148893
8 16 0.37663406 0.10195991
Starting to test, Testfile: ./tests/klein_5.train
Writing output in: ./tests/klein_5.train.cv
Algorithm : CV
Global metric : Overlap
Deviant Feature Metrics:(none)
Weighting : GainRatio
Feature 1 : 0.216759580252811
Feature 2 : 0.229247034465546
Feature 3 : 0.208759480370678
Feature 4 : 0.178473640641812
Feature 5 : 0.186311005227154
Feature 6 : 0.296622220584837
Feature 7 : 0.211488928619078
Feature 8 : 0.101959907286607
Tested: 1 @ Mon Sep 2 15:22:38 2019
Tested: 2 @ Mon Sep 2 15:22:38 2019
Tested: 3 @ Mon Sep 2 15:22:38 2019
Tested: 4 @ Mon Sep 2 15:22:38 2019
Tested: 5 @ Mon Sep 2 15:22:38 2019
Tested: 6 @ Mon Sep 2 15:22:38 2019
Tested: 7 @ Mon Sep 2 15:22:38 2019
Tested: 8 @ Mon Sep 2 15:22:38 2019
Ready: 8 @ Mon Sep 2 15:22:38 2019
Seconds taken: 0.0002 (44444.44 p/s)
Scores per Value Class:
class | TP FP TN FN precision recall(TPR) FPR F-score AUC
I | 4 0 4 0 1.00000 1.00000 0.00000 1.00000 1.00000
O | 4 0 4 0 1.00000 1.00000 0.00000 1.00000 1.00000
B | 0 0 8 0 (nan) (nan) 0.00000 (nan) (nan)
F-Score beta=1, microav: 1.000000
F-Score beta=1, macroav: 1.000000
AUC, microav: 1.000000
AUC, macroav: 1.000000
overall accuracy: 1.000000 (8/8)