Model Evaluation Skills Tests

Model Evaluation — Beginner

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.

Model Evaluation — Intermediate

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.

Model Evaluation — Advanced

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.