Performance Evaluation of Machine Learning Algorithm in Various Datasets

Abstract

Machine learning is one of the fast-growing areas of computer science, with far-reaching applications. There are several applications for machine learning. The most significant of which is supervised learning. Supervised learning is common in classification problems. In this study, frequently used twelve machine learning algorithms are considered: NB, LDA, LR, ANN, SVM, K-NN, HT, DT, C4.5, CART, RF and BB. We apply these algorithms on seven datasets. The main goal of this study was to evaluate the performance of the machine learning algorithms on both binary and multiple classification problems using a variety of performance metrics: accuracy, kappa statistic, precision, recall, specificity, F-measure, MAE, RMSE and MCC. Here, we found that RF algorithm proved to have the best performance in three out of seven datasets. But the other four algorithms: NN, NB, BB and LR also performed well.