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Imbalanced-Data
How to Handle Imbalanced Classes with XGBoost in Python
A beginner-friendly XGBoost classification workflow for rare events: expose the accuracy trap, calculate class weight from training labels, select an alert threshold on validation data, and compare the resulting precision, recall, and workload on untouched test rows.
July 22, 2026 in Python, Machine Learning by Mehdi Lotfinejad12 minutes
How to Set Naive Bayes Class Priors After Rebalancing Data
Learn why a balanced training sample can give Gaussian Naive Bayes the wrong starting probabilities, replace them with expected deployment rates, and verify the effect on an untouched rare-event test set.
July 22, 2026 in Python, Machine Learning by Mehdi Lotfinejad11 minutes