Towards Interpretable Legendary Pokémon Classification: A Comparative Evaluation of Machine Learning Models and Explainable AI

1. A comprehensive comparative evaluation of eight ML algorithms is conducted using a stratified five-fold Cross-Validation (CV) framework. The experimental findings demonstrate the superior effectiveness of gradient-boosting models, particularly LightGBM and XGBoost, in accurately classifying Legendary Pokémon and handling imbalanced gaming datasets.
2. A comprehensive ML framework is developed for Legendary Pokémon classification, integrating data preprocessing, feature scaling, missing value handling, and SMOTE-based class balancing to improve prediction reliability.
3. Integration of Explainable Artificial Intelligence (XAI) for Model Transparency:
SHAP and LIME are incorporated to provide both global and local explanations of model predictions, enabling transparent identification of the key attributes influencing Legendary Pokémon classification.