Cross-Validation and Post-Model Workflow

In this chapter, we will cover the following recipes:

• Selecting a model with cross-validation
• K-fold cross-validation
• Balanced cross-validation
• Cross-validation with ShuffleSplit
• Time series cross-validation
• Grid search with scikit-learn
• Randomized search with scikit-learn
• Classification metrics
• Regression metrics
• Clustering metrics
• Using dummy estimators to compare results
• Feature selection
• Feature selection on L1 norms
• Persisting models with joblib or pickle