XGBoost vs. LightGBM: An XAI Approach to National Vehicle Fleet Analysis

Abstract

This study analyzes the factors associated with vehicle technology classification in Ecuador, using fuel category (electric, hybrid, and internal combustion) as the dependent variable under an Explainable Artificial Intelligence (XAI) approach. Following the CRISP-DM methodology, we compared the performance of XGBoost and LightGBM algorithms using a dataset of 482,754 administrative records from the Internal Revenue Service (SRI). Both models achieved outstanding predictive performance with a Macro F1-score of 0.987, demonstrating robustness despite the severe class imbalance (electric vehicles represent only 1.3% of the total). The integration of SHAP (SHapley Additive exPlanations) values identified tax appraisal and engine displacement as the most influential features in the model predictions in the adoption of electric vehicles. In contrast, territorial factors exert a more significant influence on the acquisition of hybrid vehicles. Finally, the findings demonstrate that boosting models, combined with XAI techniques, provide transparent analytical tools that can support evidence-based transport decarbonization strategies in emerging economies.