Model Evaluation

A model is only as good as your measurement of its performance. This skill ensures you use the right metrics for your data distribution.

Classification Metrics

• Imbalanced Data: Use F1-Score, Precision-Recall AUC, or Balanced Accuracy instead of pure Accuracy.
• Cost of Mistakes: Use Recall if false negatives are expensive (e.g., medical diagnosis). Use Precision if false positives are expensive (e.g., spam filtering).

Regression Metrics

• MAE: Mean Absolute Error (Robust to outliers).
• RMSE: Root Mean Squared Error (Penalizes large errors heavily).
• R-Squared: Explains the variance captured by the model.

Validation Strategies

• K-Fold: Standard for robustness.
• Stratified K-Fold: Essential for imbalanced classification.
• Time-Series Split: Mandatory for data with a temporal component to avoid looking into the future.