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<span id="id1"></span><h1>1. Supervised learning<a class="headerlink" href="#supervised-learning" title="Permalink to this headline">ΒΆ</a></h1>
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<li class="toctree-l1"><a class="reference internal" href="modules/linear_model.html">1.1. Generalized Linear Models</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#ordinary-least-squares">1.1.1. Ordinary Least Squares</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#ordinary-least-squares-complexity">1.1.1.1. Ordinary Least Squares Complexity</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#ridge-regression">1.1.2. Ridge Regression</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#ridge-complexity">1.1.2.1. Ridge Complexity</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#setting-the-regularization-parameter-generalized-cross-validation">1.1.2.2. Setting the regularization parameter: generalized Cross-Validation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#lasso">1.1.3. Lasso</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#setting-regularization-parameter">1.1.3.1. Setting regularization parameter</a><ul>
<li class="toctree-l4"><a class="reference internal" href="modules/linear_model.html#using-cross-validation">1.1.3.1.1. Using cross-validation</a></li>
<li class="toctree-l4"><a class="reference internal" href="modules/linear_model.html#information-criteria-based-model-selection">1.1.3.1.2. Information-criteria based model selection</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#lars-lasso">1.1.7. LARS Lasso</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#mathematical-formulation">1.1.7.1. Mathematical formulation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#orthogonal-matching-pursuit-omp">1.1.8. Orthogonal Matching Pursuit (OMP)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#bayesian-regression">1.1.9. Bayesian Regression</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#bayesian-ridge-regression">1.1.9.1. Bayesian Ridge Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#automatic-relevance-determination-ard">1.1.9.2. Automatic Relevance Determination - ARD</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#logistic-regression">1.1.10. Logistic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#stochastic-gradient-descent-sgd">1.1.11. Stochastic Gradient Descent - SGD</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#perceptron">1.1.12. Perceptron</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#passive-aggressive-algorithms">1.1.13. Passive Aggressive Algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#robustness-regression-outliers-and-modeling-errors">1.1.14. Robustness regression: outliers and modeling errors</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#ransac-random-sample-consensus">1.1.14.2. RANSAC: RANdom SAmple Consensus</a><ul>
<li class="toctree-l4"><a class="reference internal" href="modules/linear_model.html#details-of-the-algorithm">1.1.14.2.1. Details of the algorithm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html#shrinkage">1.2.3. Shrinkage</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html#estimation-algorithms">1.2.4. Estimation algorithms</a></li>
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<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><ul>
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<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#complexity">1.4.4. Complexity</a></li>
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<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#custom-kernels">1.4.6.1. Custom Kernels</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="modules/svm.html#parameters-of-the-rbf-kernel">1.4.6.1.3. Parameters of the RBF Kernel</a></li>
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<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#svc">1.4.7.1. SVC</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#nusvc">1.4.7.2. NuSVC</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#svr">1.4.7.3. SVR</a></li>
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<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 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#stochastic-gradient-descent-for-sparse-data">1.5.3. Stochastic Gradient Descent for sparse data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#complexity">1.5.4. Complexity</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#tips-on-practical-use">1.5.5. Tips on Practical Use</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#mathematical-formulation">1.5.6. Mathematical formulation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/sgd.html#id1">1.5.6.1. SGD</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#implementation-details">1.5.7. Implementation details</a></li>
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<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><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#finding-the-nearest-neighbors">1.6.1.1. Finding the Nearest Neighbors</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#kdtree-and-balltree-classes">1.6.1.2. KDTree and BallTree Classes</a></li>
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<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><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#brute-force">1.6.4.1. Brute Force</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#k-d-tree">1.6.4.2. K-D Tree</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#ball-tree">1.6.4.3. Ball Tree</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#choice-of-nearest-neighbors-algorithm">1.6.4.4. Choice of Nearest Neighbors Algorithm</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#effect-of-leaf-size">1.6.4.5. Effect of <tt class="docutils literal"><span class="pre">leaf_size</span></tt></a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html#nearest-centroid-classifier">1.6.5. Nearest Centroid Classifier</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#nearest-shrunken-centroid">1.6.5.1. Nearest Shrunken Centroid</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html#approximate-nearest-neighbors">1.6.6. Approximate Nearest Neighbors</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#locality-sensitive-hashing-forest">1.6.6.1. Locality Sensitive Hashing Forest</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#mathematical-description-of-locality-sensitive-hashing">1.6.6.2. Mathematical description of Locality Sensitive Hashing</a></li>
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<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#examples">1.7.1. Examples</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/gaussian_process.html#an-introductory-regression-example">1.7.1.1. An introductory regression example</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/gaussian_process.html#fitting-noisy-data">1.7.1.2. Fitting Noisy Data</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#mathematical-formulation">1.7.2. Mathematical formulation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/gaussian_process.html#the-initial-assumption">1.7.2.1. The initial assumption</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/gaussian_process.html#the-best-linear-unbiased-prediction-blup">1.7.2.2. The best linear unbiased prediction (BLUP)</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/gaussian_process.html#the-empirical-best-linear-unbiased-predictor-eblup">1.7.2.3. The empirical best linear unbiased predictor (EBLUP)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#correlation-models">1.7.3. Correlation Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#regression-models">1.7.4. Regression Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#implementation-details">1.7.5. Implementation details</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/cross_decomposition.html">1.8. Cross decomposition</a></li>
<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#bernoulli-naive-bayes">1.9.3. Bernoulli Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html#out-of-core-naive-bayes-model-fitting">1.9.4. 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>
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<li class="toctree-l3"><a class="reference internal" href="modules/ensemble.html#random-forests">1.11.2.1. Random Forests</a></li>
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<li class="toctree-l3"><a class="reference internal" href="modules/ensemble.html#interpretation">1.11.4.7. Interpretation</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="modules/multiclass.html#one-vs-one">1.12.3. One-Vs-One</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html#l1-based-feature-selection">1.13.4. L1-based feature selection</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/feature_selection.html#selecting-non-zero-coefficients">1.13.4.1. Selecting non-zero coefficients</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html#tree-based-feature-selection">1.13.5. Tree-based feature selection</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/label_propagation.html">1.14. Semi-Supervised</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/label_propagation.html#label-propagation">1.14.1. Label Propagation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/isotonic.html">1.15. Isotonic regression</a></li>
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