ML Foundations
How to Know If Your Model Actually Works — Beyond Accuracy
Choosing the right metric and evaluation strategy is critical. A model with 99% accuracy might be useless if the data is imbalanced.
- Precision and Recall — When false positives and false negatives matter differently
- Cross-Validation — Getting reliable performance estimates
- Bias-Variance Tradeoff — The central challenge in machine learning
"Not everything that counts can be counted, and not everything that can be counted counts."
Model Evaluation — Complete Guide
Choosing the right metric and evaluation strategy is critical. A model with 99% accuracy might be useless if the data is imbalanced.
Classification Metrics
ROC Curve and AUC
Cross-Validation
Bias-Variance Tradeoff
Regression Metrics Comparison
Key Takeaways
What to Learn Next
-> Regularization Prevent overfitting with Ridge, Lasso, and Elastic Net.
-> Model Selection Hyperparameter tuning, grid search, and choosing the best model.
-> Ensemble Methods Bagging, boosting, and stacking for stronger models.