Grid Search-driven Hyperparameter Optimization for Random Forest Model in Text Analysis
Year: 2025
Type: article
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.
Authors: LILIBETH P. CORONEL, WENCESLAO A. CORONADO, ANGELA GRACE E. SINGSON
Institutions: Mindanao State University Naawan, Mindanao State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7107967343
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