5. Model Selection

• 5.1. Cross-Validation: evaluating estimator performance
  • 5.1.1. Computing cross-validated metrics
  • 5.1.2. Cross validation iterators
    • 5.1.2.1. K-fold
    • 5.1.2.2. Stratified K-Fold
    • 5.1.2.3. Leave-One-Out - LOO
    • 5.1.2.4. Leave-P-Out - LPO
    • 5.1.2.5. Leave-One-Label-Out - LOLO
    • 5.1.2.6. Leave-P-Label-Out
    • 5.1.2.7. Random permutations cross-validation a.k.a. Shuffle & Split
    • 5.1.2.8. See also
    • 5.1.2.9. Bootstrapping cross-validation
  • 5.1.3. Cross validation and model selection
• 5.2. Grid Search: setting estimator parameters
  • 5.2.1. GridSearchCV
  • 5.2.2. Examples
  • 5.2.3. Alternatives to brute force grid search
    • 5.2.3.1. Model specific cross-validation
      • 8.15.1.5. sklearn.linear_model.RidgeCV
      • 8.15.1.4. sklearn.linear_model.RidgeClassifierCV
      • 8.15.1.12. sklearn.linear_model.LarsCV
      • 8.15.1.13. sklearn.linear_model.LassoLarsCV
      • 8.15.1.7. sklearn.linear_model.LassoCV
      • 8.15.1.9. sklearn.linear_model.ElasticNetCV
    • 5.2.3.2. Information Criterion
      • 8.15.1.14. sklearn.linear_model.LassoLarsIC
    • 5.2.3.3. Out of Bag Estimates
      • 8.6.1. sklearn.ensemble.RandomForestClassifier
      • 8.6.2. sklearn.ensemble.RandomForestRegressor
      • 8.6.3. sklearn.ensemble.ExtraTreesClassifier
      • 8.6.4. sklearn.ensemble.ExtraTreesRegressor
      • 8.6.5. sklearn.ensemble.GradientBoostingClassifier
      • 8.6.6. sklearn.ensemble.GradientBoostingRegressor
• 5.3. Pipeline: chaining estimators
  • 5.3.1. Usage
  • 5.3.2. Notes