The goal of this code is to train and compare the performance of three popular classifiers (e.g., Logistic Regression, Random Forests, and Boosting) on a publicly available data set.
Steps include: Feature extraction through statistics tests entropy-based feature selection, training, tuning hyper paramaters and testing classifiers and comparing the performance of the classifiers based on different metrics.