文章基本信息

标题：Predictive Comparison Between Random Machines and Random Forest
本地全文：下载
作者：Mateus Maia ; Arthur R.Azevedo ; Anderson Ara 等
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2021
卷号：19
期号：9
页码：593-614
DOI：10.6339/21-JDS1025
语种：English
出版社：Tingmao Publish Company
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.