Evaluation is a critical step in any machine learning project. Choosing the right metric ensures your model aligns with business goals and performs well in the real world.
Common Metrics
- Accuracy, Precision, Recall
- F1 Score and AUC-ROC
- Mean Absolute Error (MAE), RMSE
Cross-Validation Techniques
Use k-fold cross-validation or stratified sampling to prevent overfitting and ensure generalization.
Real-World Considerations
Pick metrics based on the cost of errors. For example, in healthcare, false negatives may be far worse than false positives.