Beyond the 99% Accuracy Lie: Building a Leak-Free XGBoost Pipeline for Imbalanced Data

Automated excerpt

If 98% of your dataset is Class 0 and 2% is Class 1, a model can minimize loss by completely ignoring the minority class. It picks a minority class data point at random. Isolate Oversampling: Wrap SMOTE inside imblearn. pipeline. Pipeline so validation folds remain 100% untouched during cross-validation.

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