An email classification dataset of 57 features and 2 classes, spam and non-spam, will be classified in this project using different algorithms. Execute the Main file 'EE5907_Main' to execute all Questions 1-4 and plot their results The following is done in the main file: a) Call preprocessing function from file 'preprocess.m' b) Call Beta-Bernoulli Naive Bayes function from file 'BB_Naive.m' c) Plot error curves and sensitivity and specificty plots for Beta-Bernoulli d) Call Gaussian function from file 'Gaussian_Naive.m' e) Call Logistic regression function from file 'logistic_regression.m' f) Plot error curves and sensitivity and specificty plots for Logistic Regression g) Call K-Nearest neighbor function from file 'KNN.m' h) Plot error curves and sensitivity and specificty plots for KNN
Additional File: 'error_est' computes the error rate for predicted and original ydata
NOTE: If exectution of 1 function is required, please comment all subsequent questions to decrease running time.