A comparative review of regression ensembles on drug design datasets

Abstract

Drug design datasets are usually known as hard-modeled, having a large number of features and a small number of samples. Regression types of problems are common in the drug design area. Committee machines (ensembles) have become popular in machine learning because of their good performance. In this study, the dynamics of ensembles used in regression-related drug design problems are investigated with a drug design dataset collection. The study tries to determine the most successful ensemble algorithm, the base algorithm-ensemble pair having the best/worst results, the best successful single algorithm, and the similarities of algorithms according to their performances. We also discuss whether ensembles always generate better results than single algorithms.