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scikit-learn 0.22.dev0
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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. Model evaluation: 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
