Series Overview
Model Evaluation is critical for understanding model performance. Learn metrics, cross-validation, and statistical comparison methods.
Chapters
Chapter 1: Evaluation Metrics
60-70 min | Accuracy, precision, recall, F1, AUC-ROC, regression metrics
Chapter 2: Cross-Validation
50-60 min | K-fold, stratified, time-series split, validation strategies
Chapter 3: Hyperparameter Tuning in Evaluation
60-70 min | Grid search with CV, nested cross-validation, overfitting
Chapter 4: Model Comparison
60-70 min | Statistical tests, confidence intervals, model selection
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