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<a href="" class="sk-toc-active">1. Supervised learning</a>
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<a href="modules/linear_model.html">1.1. Linear Models</a>
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</style><section id="supervised-learning">
<span id="id1"></span><h1><span class="section-number">1. </span>Supervised learning<a class="headerlink" href="#supervised-learning" title="Link to this heading">¶</a></h1>
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<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#passive-aggressive-algorithms">1.1.15. Passive Aggressive Algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#robustness-regression-outliers-and-modeling-errors">1.1.16. Robustness regression: outliers and modeling errors</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#quantile-regression">1.1.17. Quantile Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#polynomial-regression-extending-linear-models-with-basis-functions">1.1.18. Polynomial regression: extending linear models with basis functions</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html#dimensionality-reduction-using-linear-discriminant-analysis">1.2.1. Dimensionality reduction using Linear Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html#mathematical-formulation-of-the-lda-and-qda-classifiers">1.2.2. Mathematical formulation of the LDA and QDA classifiers</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html#mathematical-formulation-of-lda-dimensionality-reduction">1.2.3. Mathematical formulation of LDA dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html#shrinkage-and-covariance-estimator">1.2.4. Shrinkage and Covariance Estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html#estimation-algorithms">1.2.5. Estimation algorithms</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/kernel_ridge.html">1.3. Kernel ridge regression</a></li>
<li class="toctree-l1"><a class="reference internal" href="modules/svm.html">1.4. Support Vector Machines</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#classification">1.4.1. Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#regression">1.4.2. Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#density-estimation-novelty-detection">1.4.3. Density estimation, novelty detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#complexity">1.4.4. Complexity</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#tips-on-practical-use">1.4.5. Tips on Practical Use</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#kernel-functions">1.4.6. Kernel functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#mathematical-formulation">1.4.7. Mathematical formulation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#implementation-details">1.4.8. Implementation details</a></li>
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</li>
<li class="toctree-l1"><a class="reference internal" href="modules/sgd.html">1.5. Stochastic Gradient Descent</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#classification">1.5.1. Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#regression">1.5.2. Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#online-one-class-svm">1.5.3. Online One-Class SVM</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#stochastic-gradient-descent-for-sparse-data">1.5.4. Stochastic Gradient Descent for sparse data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#complexity">1.5.5. Complexity</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#stopping-criterion">1.5.6. Stopping criterion</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#tips-on-practical-use">1.5.7. Tips on Practical Use</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#mathematical-formulation">1.5.8. Mathematical formulation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#implementation-details">1.5.9. Implementation details</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/neighbors.html">1.6. Nearest Neighbors</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html#unsupervised-nearest-neighbors">1.6.1. Unsupervised Nearest Neighbors</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html#nearest-neighbors-classification">1.6.2. Nearest Neighbors Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html#nearest-neighbors-regression">1.6.3. Nearest Neighbors Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html#nearest-neighbor-algorithms">1.6.4. Nearest Neighbor Algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html#nearest-centroid-classifier">1.6.5. Nearest Centroid Classifier</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html#nearest-neighbors-transformer">1.6.6. Nearest Neighbors Transformer</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html#neighborhood-components-analysis">1.6.7. Neighborhood Components Analysis</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/gaussian_process.html">1.7. Gaussian Processes</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#gaussian-process-regression-gpr">1.7.1. Gaussian Process Regression (GPR)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#gaussian-process-classification-gpc">1.7.2. Gaussian Process Classification (GPC)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#gpc-examples">1.7.3. GPC examples</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#kernels-for-gaussian-processes">1.7.4. Kernels for Gaussian Processes</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/cross_decomposition.html">1.8. Cross decomposition</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_decomposition.html#plscanonical">1.8.1. PLSCanonical</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_decomposition.html#plssvd">1.8.2. PLSSVD</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_decomposition.html#plsregression">1.8.3. PLSRegression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_decomposition.html#canonical-correlation-analysis">1.8.4. Canonical Correlation Analysis</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="modules/naive_bayes.html">1.9. Naive Bayes</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html#gaussian-naive-bayes">1.9.1. Gaussian Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html#multinomial-naive-bayes">1.9.2. Multinomial Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html#complement-naive-bayes">1.9.3. Complement Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html#bernoulli-naive-bayes">1.9.4. Bernoulli Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html#categorical-naive-bayes">1.9.5. Categorical Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html#out-of-core-naive-bayes-model-fitting">1.9.6. Out-of-core naive Bayes model fitting</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/tree.html">1.10. Decision Trees</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html#classification">1.10.1. Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html#regression">1.10.2. Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html#multi-output-problems">1.10.3. Multi-output problems</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html#complexity">1.10.4. Complexity</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html#tips-on-practical-use">1.10.5. Tips on practical use</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html#tree-algorithms-id3-c4-5-c5-0-and-cart">1.10.6. Tree algorithms: ID3, C4.5, C5.0 and CART</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html#mathematical-formulation">1.10.7. Mathematical formulation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html#missing-values-support">1.10.8. Missing Values Support</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html#minimal-cost-complexity-pruning">1.10.9. Minimal Cost-Complexity Pruning</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html#gradient-boosted-trees">1.11.1. Gradient-boosted trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html#random-forests-and-other-randomized-tree-ensembles">1.11.2. Random forests and other randomized tree ensembles</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html#bagging-meta-estimator">1.11.3. Bagging meta-estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html#voting-classifier">1.11.4. Voting Classifier</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html#voting-regressor">1.11.5. Voting Regressor</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html#stacked-generalization">1.11.6. Stacked generalization</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html#adaboost">1.11.7. AdaBoost</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/multiclass.html">1.12. Multiclass and multioutput algorithms</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/multiclass.html#multiclass-classification">1.12.1. Multiclass classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multiclass.html#multilabel-classification">1.12.2. Multilabel classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multiclass.html#multiclass-multioutput-classification">1.12.3. Multiclass-multioutput classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multiclass.html#multioutput-regression">1.12.4. Multioutput regression</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/feature_selection.html">1.13. Feature selection</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html#removing-features-with-low-variance">1.13.1. Removing features with low variance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html#univariate-feature-selection">1.13.2. Univariate feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html#recursive-feature-elimination">1.13.3. Recursive feature elimination</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html#feature-selection-using-selectfrommodel">1.13.4. Feature selection using SelectFromModel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html#sequential-feature-selection">1.13.5. Sequential Feature Selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html#feature-selection-as-part-of-a-pipeline">1.13.6. Feature selection as part of a pipeline</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/semi_supervised.html">1.14. Semi-supervised learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/semi_supervised.html#self-training">1.14.1. Self Training</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/semi_supervised.html#label-propagation">1.14.2. Label Propagation</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l1"><a class="reference internal" href="modules/calibration.html">1.16. Probability calibration</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/calibration.html#calibration-curves">1.16.1. Calibration curves</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/calibration.html#calibrating-a-classifier">1.16.2. Calibrating a classifier</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/calibration.html#usage">1.16.3. Usage</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html#multi-layer-perceptron">1.17.1. Multi-layer Perceptron</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html#classification">1.17.2. Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html#regression">1.17.3. Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html#regularization">1.17.4. Regularization</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html#algorithms">1.17.5. Algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html#complexity">1.17.6. Complexity</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html#mathematical-formulation">1.17.7. Mathematical formulation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html#tips-on-practical-use">1.17.8. Tips on Practical Use</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html#more-control-with-warm-start">1.17.9. More control with warm_start</a></li>
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