3. Model selection and evaluation

• 3.1. Cross-validation: evaluating estimator performance
  • 3.1.1. Computing cross-validated metrics
  • 3.1.2. Cross validation iterators
    • 3.1.2.1. K-fold
    • 3.1.2.2. Stratified k-fold
    • 3.1.2.3. Leave-One-Out - LOO
    • 3.1.2.4. Leave-P-Out - LPO
    • 3.1.2.5. Leave-One-Label-Out - LOLO
    • 3.1.2.6. Leave-P-Label-Out
    • 3.1.2.7. Random permutations cross-validation a.k.a. Shuffle & Split
    • 3.1.2.8. See also
  • 3.1.3. A note on shuffling
  • 3.1.4. Cross validation and model selection
• 3.2. Grid Search: Searching for estimator parameters
  • 3.2.1. Exhaustive Grid Search
    • 3.2.1.1. Scoring functions for parameter search
  • 3.2.2. Randomized Parameter Optimization
  • 3.2.3. Alternatives to brute force parameter search
    • 3.2.3.1. Model specific cross-validation
      • 3.2.3.1.1. sklearn.linear_model.RidgeCV
        • 3.2.3.1.1.1. Examples using sklearn.linear_model.RidgeCV
      • 3.2.3.1.2. sklearn.linear_model.RidgeClassifierCV
      • 3.2.3.1.3. sklearn.linear_model.LarsCV
      • 3.2.3.1.4. sklearn.linear_model.LassoLarsCV
        • 3.2.3.1.4.1. Examples using sklearn.linear_model.LassoLarsCV
      • 3.2.3.1.5. sklearn.linear_model.LassoCV
        • 3.2.3.1.5.1. Examples using sklearn.linear_model.LassoCV
      • 3.2.3.1.6. sklearn.linear_model.ElasticNetCV
    • 3.2.3.2. Information Criterion
      • 3.2.3.2.1. sklearn.linear_model.LassoLarsIC
        • 3.2.3.2.1.1. Examples using sklearn.linear_model.LassoLarsIC
    • 3.2.3.3. Out of Bag Estimates
      • 3.2.3.3.1. sklearn.ensemble.RandomForestClassifier
        • 3.2.3.3.1.1. Examples using sklearn.ensemble.RandomForestClassifier
      • 3.2.3.3.2. sklearn.ensemble.RandomForestRegressor
        • 3.2.3.3.2.1. Examples using sklearn.ensemble.RandomForestRegressor
      • 3.2.3.3.3. sklearn.ensemble.ExtraTreesClassifier
        • 3.2.3.3.3.1. Examples using sklearn.ensemble.ExtraTreesClassifier
      • 3.2.3.3.4. sklearn.ensemble.ExtraTreesRegressor
        • 3.2.3.3.4.1. Examples using sklearn.ensemble.ExtraTreesRegressor
      • 3.2.3.3.5. sklearn.ensemble.GradientBoostingClassifier
        • 3.2.3.3.5.1. Examples using sklearn.ensemble.GradientBoostingClassifier
      • 3.2.3.3.6. sklearn.ensemble.GradientBoostingRegressor
        • 3.2.3.3.6.1. Examples using sklearn.ensemble.GradientBoostingRegressor
• 3.3. Pipeline: chaining estimators
  • 3.3.1. Usage
  • 3.3.2. Notes
• 3.4. FeatureUnion: Combining feature extractors
  • 3.4.1. Usage
• 3.5. Model evaluation: quantifying the quality of predictions
  • 3.5.1. The scoring parameter: defining model evaluation rules
    • 3.5.1.1. Common cases: predefined values
    • 3.5.1.2. Defining your scoring strategy from score functions
    • 3.5.1.3. Implementing your own scoring object
  • 3.5.2. Function for prediction-error metrics
    • 3.5.2.1. Classification metrics
      • 3.5.2.1.1. Accuracy score
      • 3.5.2.1.2. Confusion matrix
      • 3.5.2.1.3. Classification report
      • 3.5.2.1.4. Hamming loss
      • 3.5.2.1.5. Jaccard similarity coefficient score
      • 3.5.2.1.6. Precision, recall and F-measures
        • 3.5.2.1.6.1. Binary classification
        • 3.5.2.1.6.2. Multiclass and multilabel classification
      • 3.5.2.1.7. Hinge loss
      • 3.5.2.1.8. Log loss
      • 3.5.2.1.9. Matthews correlation coefficient
      • 3.5.2.1.10. Receiver operating characteristic (ROC)
      • 3.5.2.1.11. Zero one loss
    • 3.5.2.2. Regression metrics
      • 3.5.2.2.1. Explained variance score
      • 3.5.2.2.2. Mean absolute error
      • 3.5.2.2.3. Mean squared error
      • 3.5.2.2.4. R² score, the coefficient of determination
  • 3.5.3. Clustering metrics
  • 3.5.4. Biclustering metrics
    • 3.5.4.1. Clustering metrics
  • 3.5.5. Dummy estimators
• 3.6. Model persistence
  • 3.6.1. Persistence example
  • 3.6.2. Security & maintainability limitations
• 3.7. Validation curves: plotting scores to evaluate models
  • 3.7.1. Validation curve
  • 3.7.2. Learning curve