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Debugging a boosted model: learning curves, tree dumps, and plots that explain a prediction
A boosted-tree model can post an attractive validation score while still being wrong for the reasons that matter: leakage, an unrepresentative split, a brittle interaction, or one small segment carryi
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Tools: SHAP, LIME, InterpretML
Model interpretability is essential for building trust in machine learning. This post explores three leading interpretability tools — SHAP, LIME, and InterpretML — and how they help engineers and data scientists understand, debug, and explain complex models in 2025 and beyond.