Overview

Original Research

GRID SEARCH-DRIVEN HYPERPARAMETER OPTIMIZATION FOR RANDOM FOREST MODEL IN TEXT ANALYSIS

LILIBETH P. CORONEL, WENCESLAO A. CORONADO and ANGELA GRACE E. SINGSON

Vol 20, No 11 ( 2025 )   |  DOI: 10.5281/zenodo.17760374   |   Author Affiliation: Associate Professor V, College of Business and Information Technology, Mindanao State University at Naawan, Philippines 1, Professor VI, College of Education and Social Sciences, Mindanao State University at Naawan, Philippines 2, Graduate Studies, College of Fisheries and Marine Science, Mindanao State University at Naawan, Philippines 3   |   Licensing: CC 4.0   |   Pg no: 208-215   |   Published on: 29-11-2025

Abstract

Random Forest, a widely used ensemble learning algorithm, exhibits strong classification performance; however, its accuracy and computational efficiency are highly dependent on appropriate hyperparameter tuning. To address this, the research employs a systematic grid search optimization approach to identify the optimal combination of key Random Forest parameters. Experimental results demonstrate that the optimized Random Forest model achieved an accuracy of 98.40%, outperforming the baseline model across all key performance metrics, including precision, recall, and F1-score, with improvements of up to 0.31%. Notably, the optimized configuration also reduced training time by approximately 83.72%, underscoring the significant computational efficiency gained through systematic tuning. These findings clearly prove that structured grid search optimization not only enhances predictive performance but also reduces computational cost, making it a robust and interpretable approach for machine learning model refinement.

Keywords

Random Forest, Grid Search, Hyperparameter, Classification, Optimization, Machine Learning