Overview
Model evaluation is a critical step in the machine learning workflow. It helps us understand how well our model is performing and whether it's ready for deployment.
Different types of problems require different evaluation metrics, and understanding when to use each is essential for proper model assessment.
Key Concepts
- Classification metrics (accuracy, precision, recall, F1-score)
- Regression metrics (MSE, RMSE, MAE, R²)
- Confusion matrix and ROC curves
- Cross-validation techniques
- Bias-variance tradeoff
Practice Exercise
Exercise: Comprehensive Model Evaluation
Using a dataset of your choice:
- Build both classification and regression models
- Implement k-fold cross-validation
- Calculate and interpret appropriate evaluation metrics
- Create and interpret ROC curves and confusion matrices
- Analyze the bias-variance tradeoff in your models
Resources
Google MLCC Metrics
Main resource for today
Scikit-learn Metrics
Official documentation for evaluation metrics
ROC Curves Explained
Understanding ROC curves and AUC
Cross-Validation
Different cross-validation techniques
Complete Today's Task
Mark today's task as complete to track your progress and earn achievements.