Confidence in Random Forest for Performance Optimization

Abstract

In this paper, we present a non-deterministic strategy for searching for optimal number of trees (NoTs) hyperparameter in Random Forest (RF). Hyperparameter tuning in Machine Learning (ML) algorithms optimizes predictability of an ML algorithm and/or improves computer resources utilization. However, hyperparameter tuning is a complex optimization task and time consuming. We set up experiments with the goal of maximizing predictability, minimizing NoTs and minimizing time of execution (ToE). Compared to the deterministic algorithm, e-greedy and default configured RF, this research’s non-deterministic algorithm recorded an average percentage accuracy (acc) of approximately 98%, NoTs percentage average improvement of 29.39%, average ToE improvement ratio of 415.92 and an average improvement of 95% iterations. Moreover, evaluations using Jackknife Estimation showed stable and reliable results from several experiment runs of the non-deterministic strategy. The non-deterministic approach in selecting hyperparameter showed a significant acc 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.