Model Evaluation
Model evaluation is a crucial step in the machine learning pipeline that helps us understand how well our models perform and make informed decisions about model selection and improvement.
Performance Metrics
Classification Metrics
- Accuracy: Overall correctness of predictions
- Precision: Positive predictive value
- Recall: Sensitivity or true positive rate
- F1 Score: Harmonic mean of precision and recall
- ROC and AUC: Trade-off between true and false positive rates
Regression Metrics
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- Mean Absolute Error (MAE)
- R-squared (R²)
- Adjusted R-squared
Ranking Metrics
- Mean Average Precision
- Normalized Discounted Cumulative Gain
- Mean Reciprocal Rank
- Precision at K
Cross-Validation
Types of Cross-Validation
- K-Fold Cross-Validation
- Stratified K-Fold
- Leave-One-Out Cross-Validation
- Time Series Cross-Validation
Implementation
- Train-Test Split
- Validation Set
- Cross-Validation Score
- Cross-Validation with Parameter Tuning
Model Selection
Techniques
- Grid Search
- Random Search
- Bayesian Optimization
- Nested Cross-Validation
Criteria
- Information Criteria (AIC, BIC)
- Cross-Validation Error
- Model Complexity
- Computational Efficiency
Bias-Variance Tradeoff
Understanding the Tradeoff
- Bias: Model's assumptions about the data
- Variance: Model's sensitivity to changes in training data
- Total Error Decomposition
- Optimal Model Complexity
Diagnosing Problems
- Learning Curves
- Validation Curves
- Error Analysis
- Bias-Variance Diagnostics
Advanced Evaluation Techniques
Bootstrap Methods
- Bootstrap Sampling
- Confidence Intervals
- Out-of-Bag Estimates
- Bootstrap Aggregating
Statistical Tests
- McNemar's Test
- Wilcoxon Signed-Rank Test
- Student's t-Test
- ANOVA
Practical Considerations
Model Comparison
- Statistical Significance
- Effect Size
- Model Ranking
- Ensemble Selection
Production Deployment
- Model Monitoring
- Performance Tracking
- A/B Testing
- Model Updates