Model Evaluation
Back: ML Fundamentals
How to measure whether a model is performing well, avoid overfitting, and tune for optimal performance. Proper evaluation methodology is critical for building reliable ML systems.
Concepts
- Train-Validation-Test Split
- Classification Metrics
- Regression Metrics
- Bias-Variance Tradeoff
- Regularization
- Hyperparameter Tuning
- Feature Importance