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Advanced ML evaluation: nested CV pitfalls, calibration error metrics, fairness-aware reporting, bootstrap and paired tests, off-policy and slice analysis, and probabilistic forecasting scores. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Intermediate ML evaluation: stratified splits, precision–recall tradeoffs, macro vs micro metrics, calibration basics, learning curves, threshold tuning, and regression diagnostics. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Foundations of ML model evaluation: train/validation/test splits, confusion matrix, precision and recall, overfitting, and choosing sensible baselines. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
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