JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1025 10.6339/21-JDS1025 Statistical Data Science Predictive Comparison Between Random Machines and Random Forests MaiaMateus1 AzevedoArthur R.2 AraAndersonalsouzara@gmail.com3∗ Department of Math & Statistics, Maynooth University, Maynooth, Ireland Department of Statistics, Federal University of Bahia, Salvador-BA, Brazil Department of Statistics, Federal University of Paraná, Curitiba-PR, Brazil Corresponding author. Email: alsouzara@gmail.com. 202128102021194593614 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.

5820211992021 2021 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2021 Open access article under the CC BY license.

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.

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