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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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XGBoost pitfalls: the mistakes that quietly wreck a boosted model
A boosted-tree model can look healthy while being wrong in ways ordinary metrics do not expose. It may overfit after the useful trees were built, leak the test set into tuning, scramble columns at inf