ex7_2_4.R

# exercise 7.2.4
rm(list=ls())
source('setup.R')
library(cvTools)
# Load data
source("Scripts/ex7_2_3.R")

# K-fold crossvalidation
K <- 10;
set.seed(1234) # for reproducibility
CV <- cvFolds(N, K=K)
# set up vectors that will store sizes of training and test sizes
CV$TrainSize <- c()
CV$TestSize <- c()


# Parameters for naive Bayes classifier
Distribution <- c('normal', 'mvmn', 'normal', 'mvmn');
Prior <- 'empirical';

# Variable for classification error
Error <- rep(NA, times=K)

for(k in 1:K){ # For each crossvalidation fold
    print(paste('Crossvalidation fold ', k, '/', K, sep=''));

    # Extract training and test set
    X_train <- X[CV$which!=k, ];
    y_train <- y[CV$which!=k]
    X_test <- X[CV$which==k, ];
    y_test <- y[CV$which==k]
    CV$TrainSize[k] <- length(y_train)
    CV$TestSize[k] <- length(y_test)
    
    X_traindf <- data.frame(X_train)
    colnames(X_traindf) <- attributeNames
    X_testdf <- data.frame(X_test)
    colnames(X_testdf) <- attributeNames
    # Fit naive Bayes classifier to training set

    mymod <- naiveBayes(X_train, y_train, distribution=Distribution, prior=Prior)
    # Predict model on test data    
    predictRes <- predict.naiveBayes(Xtest=X_test, mod=mymod)
    y_test_est <- predictRes$predictedClass
    
    # Compute error rate
    err <- y_test!=y_test_est
    err[is.na(err)] <- 1 # make test cases that could not be predicted count as errors
    Error[k] <- sum(err) # Count the number of errors
  }

# Print the error rate
print(paste('Error rate: ', sum(Error)/sum(CV$TestSize)*100), sep='');