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<li class="toctree-l1 has-children"><a class="reference internal" href="supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html">1.1. Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_ridge.html">1.3. Kernel ridge regression</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/tree.html">1.10. Decision Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html">1.13. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/mixture.html">2.1. Gaussian mixture models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html">2.2. Manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/density.html">2.8. Density Estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
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<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">3. Model selection and evaluation</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
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<li class="toctree-l1"><a class="reference internal" href="visualizations.html">5. Visualizations</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html">6.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html">6.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html">6.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html">6.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
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<li class="toctree-l2"><a class="reference internal" href="computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
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<li class="toctree-l1"><a class="reference internal" href="model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/array_api.html">11.1. Array API support (experimental)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="presentations.html">13. External Resources, Videos and Talks</a></li>
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<span id="model-selection"></span><h1><span class="section-number">3. </span>Model selection and evaluation<a class="headerlink" href="#model-selection-and-evaluation" title="Link to this heading">#</a></h1>
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<li class="toctree-l1"><a class="reference internal" href="modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html#computing-cross-validated-metrics">3.1.1. Computing cross-validated metrics</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html#a-note-on-shuffling">3.1.3. A note on shuffling</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html#cross-validation-and-model-selection">3.1.4. Cross validation and model selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html#permutation-test-score">3.1.5. Permutation test score</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html#exhaustive-grid-search">3.2.1. Exhaustive Grid Search</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/classification_threshold.html#post-tuning-the-decision-threshold">3.3.1. Post-tuning the decision threshold</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#which-scoring-function-should-i-use">3.4.1. Which scoring function should I use?</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#scoring-api-overview">3.4.2. Scoring API overview</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#the-scoring-parameter-defining-model-evaluation-rules">3.4.3. The <code class="docutils literal notranslate"><span class="pre">scoring</span></code> parameter: defining model evaluation rules</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#classification-metrics">3.4.4. Classification metrics</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#multilabel-ranking-metrics">3.4.5. Multilabel ranking metrics</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#regression-metrics">3.4.6. Regression metrics</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#clustering-metrics">3.4.7. Clustering metrics</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#dummy-estimators">3.4.8. Dummy estimators</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html#validation-curve">3.5.1. Validation curve</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html#learning-curve">3.5.2. Learning curve</a></li>
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