Enhancing Energy Efficiency and Classification Modeling Through a Combined Approach of LightGBM and Stratified KFold Cross-Validation

This research article aims to develop a robust and interpretable model for energy efficiency and classification that overcomes the limitations of existing techniques. The study's objectives are to address the challenges faced by the established methods such as linear regression, random forest, support vector machines, and others. These limitations include issues related to predictive accuracy, model interpretability, and computational efficiency. To bridge these gaps, we introduce a novel model, stratified KFold LightGBM ensemble (SKL-EEFE). SKL-EEFE leverages the strengths of stratified KFold cross-validation, LightGBM gradient boosting, domain-specific feature engineering, and ensemble learning to provide a comprehensive solution to classification tasks. Our key findings reveal that SKL-EEFE outperforms existing methods while maintaining model interpretability. The proposed model offers both accuracy and transparency, making it a valuable addition to the field of classification modeling. This research contributes to advancing the state of knowledge by offering an innovative approach to overcome the limitations of existing models and providing a practical solution for real-world classification problems.

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