Prediction accuracy analysis with logistic regression and CART decision tree

Abstract

Classification is one of the most important techniques in machine learning. In classification problems, logistic regression and decision tree are two efficient algorithms in supervised learning. In this paper, we tested logical regression and CART decision tree algorithms on different datasets. The results received from experiments showed that CART decision tree performs much better in data set with more attributes and slight imbalanced data distribution. At the same time logistic regression is more accurate on datasets with fewer attributes and balanced data distribution.