Predictive Comparison Between Random Machines and Random Forests

Journal of Data Science

article

Mateus Maia¹  Arthur R. Azevedo²  Anderson Ara³ 

[1] Department of Math & Statistics, Maynooth University;Department of Statistics, Federal University of Bahia;Department of Statistics, Federal University of Paraná

关键词: bagging;    ensemble;    support vector machines;   

DOI  :  10.6339/21-JDS1025

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

Ensemble techniques have been gaining strength among machine learning models, considering supervised tasks, due to their great predictive capacity when compared with some traditional approaches. The random forest is considered to be one of the off-the-shelf algorithms due to its flexibility and robust performance to both regression and classification tasks. In this paper, the random machines method is applied over simulated data sets and benchmarking datasets in order to be compared with the consolidated random forest models. The results from simulated models show that the random machines method has a better predictive performance than random forest in most of the investigated data sets. Three real data situations demonstrate that the random machines may be used to solve real-world problems with competitive payoff.

【 授权许可】

CC BY