Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 73–80 (2018)
Abstract. In this paper, we present a non-deterministic strategy for searching for optimal number of trees hyperparameter in Random Forest (RF). Hyperparameter tuning in Machine Learning (ML) algorithms is essential. It optimizes predictability of an ML algorithm and/or improves computer resources utilization. However, hyperparameter tuning is a complex optimization task and time wasting. We set up experiments with the goal of maximizing predictability, minimizing number of trees and minimizing time of execution. Compared to the deterministic algorithm, this research's non-deterministic algorithm recorded an average percentage accuracy of approximately 98\\%, number of trees percentage average improvement of 44.64\\%, average time of execution mean improvement ratio of 212.79 and an average improvement of 93\\% iterations. Moreover, evaluations using Jackkife Estimation show stable and reliable results from several experiment runs of the non-deterministic strategy. The non-deterministic approach in selecting hyperparameter shows a significant accuracy and better computer resources (i.e cpu and memory time) utilization. This approach can be adopted widely in hyperparameter tuning, and in conserving utilization of computer resources i.e green computing.
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