Predictive Comparison Between Random Machines and Random Forests

Maia, Mateus; Azevedo, Arthur R.; Ara, Anderson

doi:10.6339/21-JDS1025

Journal of Data Science

Predictive Comparison Between Random Machines and Random Forests

Volume 19, Issue 4 (2021), pp. 593–614

Mateus Maia Arthur R. Azevedo Anderson Ara

https://doi.org/10.6339/21-JDS1025

Pub. online: 28 October 2021 Type: Statistical Data Science

Received
5 August 2021

Accepted
19 September 2021

Published
28 October 2021

Abstract

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.

Supplementary material

Supplementary Material A

The RM was also implemented in R language and it can be used through the rmachines package, available and documented at GitHub https://github.com/MateusMaiaDS/rmachines. To a overall description of how to reproduce the results from this article just access the README at https://mateusmaiads.github.io/rmachines_and_randomforest/.

Supplementary Material B

Exposes a descriptive analysis of the three real-world applications displayed in Section 5 and additional results around the comparison of RM and RF.

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Keywords

bagging ensemble support vector machines

Funding

The authors gratefully acknowledge the financial support of the Brazilian research funding agencies CAPES (Federal Agency for the Support and Improvement of Higher Education). M.M.’s work was supported by a Science Foundation Ireland Career Development Award Grant 17/CDA/4695.

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