xgboost.R

# XGBoost

# Setting the Working Directory
setwd('./Documents/Machine Learning A-Z/Part 10 - Model Selection/Section 53 - Combining Several Models')

# Importing the Dataset
dataset = read.csv('Churn_Modelling.csv')
dataset = dataset[4:14]

# Encoding the categorical Variables as Factors
dataset$Geography = as.numeric(factor(dataset$Geography,
                                      levels = c('France', 'Spain', 'Germany'),
                                      labels = c(1, 2, 3)))
dataset$Gender = as.numeric(factor(dataset$Gender,
                                   levels = c('Female', 'Male'),
                                   labels = c(1, 2)))

# Splitting the Dataset into the Training Set and Test Set

library(caTools)
set.seed(123)
split = sample.split(dataset$Exited, SplitRatio = 0.8)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

# Fitting XGBoost to the Training Set

library(xgboost)
classifier = xgboost(data = as.matrix(training_set[-11]), label = training_set$Exited, nrounds = 10)
classifier

# Predicting the Test Set Results

y_pred = predict(classifier, newdata = as.matrix(test_set[-11]))
y_pred = (y_pred >= 0.5)
y_pred

# Making the Confusion Matrix
cm = table(test_set[, 11], y_pred)
cm

# Applying k-Fold Cross Validation

library(caret)

folds = createFolds(training_set$Exited, k = 10)
folds 

cv = lapply(folds, function(x) {
  training_fold = training_set[-x, ]
  test_fold = training_set[x, ]
  classifier = xgboost(data = as.matrix(training_set[-11]), label = training_set$Exited, nrounds = 10)
  y_pred = predict(classifier, newdata = as.matrix(test_fold[-11]))
  y_pred = (y_pred >= 0.5)
  cm = table(test_fold[, 11], y_pred)
  accuracy = (cm[1,1] + cm[2,2]) / (cm[1,1] + cm[2,2] + cm[1,2] + cm[2,1])
  return(accuracy)
})
cv

accuracy = mean(as.numeric(cv))