<ul><li>Tree-based models like random forests are favored over deep learning for tabular data due to their prediction performance and efficiency.</li><li>Local Feature Importances (LFI) methods such as LIME and TreeSHAP provide sample-specific explanations but have limitations.</li><li>MDI+ is a global feature importance method but lacks explanations for predictions with diverse individual characteristics.</li><li>To address this, Local MDI+ (LMDI+) has been introduced, outperforming LIME and TreeSHAP in identifying instance-specific signal features and enhancing interpretability.</li></ul>

Local MDI+: Local Feature Importances for Tree-Based Models

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