Validation & Metrics
Validation & Metrics
Evaluating and measuring model performance systematically.
Learning Outcomes
By completing this topic, you will:
- Implement cross-validation strategies
- Choose appropriate metrics for your problem
- Avoid common evaluation mistakes
- Design validation for production systems
Visual Guides
ROC Curve
Precision-Recall Tradeoff
Cross-Validation
Prerequisites
- Supervised Learning concepts
- Classification and Regression basics
- Understanding of overfitting
Key Concepts
Cross-Validation
Robust model evaluation:
- K-fold: Split data into K parts, rotate test set
- Stratified: Preserve class distribution in folds
- Time series: Respect temporal ordering
Classification Metrics
| Metric | Best For |
|---|---|
| Accuracy | Balanced classes |
| Precision | Cost of false positives high |
| Recall | Cost of false negatives high |
| F1-score | Balance precision and recall |
| ROC-AUC | Overall discrimination |
Regression Metrics
- MSE/RMSE: Penalizes large errors
- MAE: Robust to outliers
- R-squared: Explained variance
When to Use
Validation depth depends on:
- Model complexity and risk
- Data size and variability
- Deployment requirements
- Regulatory constraints
Common Pitfalls
- Using accuracy on imbalanced data
- Data leakage during preprocessing
- Optimizing wrong metric for business
- Not holding out final test set
- Ignoring variance across folds
(c) Joerg Osterrieder 2025