# Install these packages
# pkgs = c("klaR", "caret", "ElemStatLearn")
# install.packages(pkgs)

library('ElemStatLearn')
library("klaR")
library("caret")

# Split the data in training and testing
inx = sample(nrow(spam), round(nrow(spam) * 0.9))
train = spam[inx,]
test = spam[-inx,]

# Define a matrix with features, X_train
# And a vector with class labels, y_train
X_train = train[,-58]
y_train = train$spam

X_test = test[,-58]
y_test = test$spam

# Train the model
nb_model = train(X_train, y_train, method = 'nb',
              trControl=trainControl(method='cv', number=3))

# Compute
preds = predict(nb_model$finalModel, X_test)$class
tbl = table(y_test, yhat=preds)
sum(diag(tbl)) / sum(tbl)
# 0.7217391

png('accuracy_nb.png')
plot(nb_model)
dev.off()