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scikit-learn 1.3.dev0
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  • User Guide
    • 1. Supervised learning
    • 2. Unsupervised learning
    • 3. Model selection and evaluation
      • 3.1. Cross-validation: evaluating estimator performance
      • 3.2. Tuning the hyper-parameters of an estimator
      • 3.3. Metrics and scoring: quantifying the quality of predictions
      • 3.4. Validation curves: plotting scores to evaluate models
    • 4. Inspection
    • 5. Visualizations
    • 6. Dataset transformations
    • 7. Dataset loading utilities
    • 8. Computing with scikit-learn
    • 9. Model persistence
    • 10. Common pitfalls and recommended practices
    • 11. Dispatching

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
    • 3.1.5. Permutation test score
  • 3.2. Tuning the hyper-parameters of an estimator
    • 3.2.1. Exhaustive Grid Search
    • 3.2.2. Randomized Parameter Optimization
    • 3.2.3. Searching for optimal parameters with successive halving
    • 3.2.4. Tips for parameter search
    • 3.2.5. 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. Validation curves: plotting scores to evaluate models
    • 3.4.1. Validation curve
    • 3.4.2. Learning curve
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