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scikit-learn 0.23.2
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  • 3. Model selection and evaluation

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
    • 3.1.3. A note on shuffling
    • 3.1.4. Cross validation and model selection
  • 3.2. Tuning the hyper-parameters of an estimator
    • 3.2.1. Exhaustive Grid Search
    • 3.2.2. Randomized Parameter Optimization
    • 3.2.3. Tips for parameter search
    • 3.2.4. Alternatives to brute force parameter search
  • 3.3. Metrics and scoring: quantifying the quality of predictions
    • 3.3.1. The scoring parameter: defining model evaluation rules
    • 3.3.2. Classification metrics
    • 3.3.3. Multilabel ranking metrics
    • 3.3.4. Regression metrics
    • 3.3.5. Clustering metrics
    • 3.3.6. Dummy estimators
  • 3.4. Model persistence
    • 3.4.1. Persistence example
    • 3.4.2. Security & maintainability limitations
  • 3.5. Validation curves: plotting scores to evaluate models
    • 3.5.1. Validation curve
    • 3.5.2. Learning curve
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