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ARTICLE
OPTIMIZING RANDOM FOREST REGRESSOR PERFORMANCE IN BEEF QUALITY PREDICTION THROUGH HYPERPARAMETER TUNING
Issue Vol. 1 No. 01 (2024): Volume 01 Issue 01 --- Section Articles --- Published Date: 2024-12-23
Abstract
The continuous escalation in global meat consumption, particularly beef, underscores the critical need for sophisticated and efficient methodologies to ascertain and predict meat quality attributes. Traditional approaches to quality assessment, often reliant on invasive and laborious laboratory analyses, present significant logistical and economic challenges within the modern meat industry. In response to these limitations, the integration of advanced machine learning paradigms, specifically ensemble learning models such as the Random Forest Regressor (RFR), has emerged as a highly promising avenue for developing rapid, non-destructive, and precise prediction models. However, the inherent complexity of these models implies that their predictive efficacy is profoundly contingent upon the meticulous configuration of their intrinsic hyperparameters. This comprehensive investigation meticulously explores the intricate relationship between hyperparameter tuning and the predictive accuracy of an RFR model specifically tailored for beef quality assessment.
Leveraging a robust dataset encompassing a diverse array of physico-chemical parameters and spectral signatures obtained via Near-Infrared (NIR) spectroscopy, a rigorous and systematic hyperparameter optimization strategy was implemented. This strategy prominently featured Randomized Search Cross-Validation, a statistically efficient technique designed to traverse a wide parameter space. The specific hyperparameters subjected to optimization included the number of constituent trees (n_estimators), the criteria for feature selection at each node split (max_features), the minimum number of data samples mandated for a leaf node (min_samples_leaf), and the requisite minimum samples for an internal node to undergo splitting (min_samples_split). The empirical findings unequivocally demonstrate a marked enhancement in the RFR model's overall predictive performance post-tuning. This improvement is robustly evidenced by substantial increases in the Coefficient of Determination (R2) values across all predicted beef quality attributes, complemented by significant reductions in error metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The average R2 improvement across all parameters was approximately 14% compared to models using default parameters. These compelling results accentuate the indispensable role of comprehensive hyperparameter optimization in cultivating high-performing, resilient, and accurate predictive models for the multifaceted domain of beef quality assessment.
Keywords
References
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