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 consuming. We set up experiments with the goal of
maximizing predictability, minimizing number of trees and minimizing
time of execution. Compared to the deterministic search
algorithm, the non-deterministic search 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 175.62 and an
average improvement of 94% iterations. Moreover, evaluations
using Jackknife Estimation show stable and reliable results from
several experiment runs of the non-deterministic strategy. The
non-deterministic approach in searching 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 like green computing.