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<h3>Contents</h3>
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<li><a class="reference internal" href="#">3. Supervised learning</a><ul>
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<span id="id1"></span><h1>3. 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">3.1. Generalized Linear Models</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#ordinary-least-squares-ols">3.1.1. Ordinary Least Squares (OLS)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#ols-complexity">3.1.1.1. OLS Complexity</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#ridge-regression">3.1.2. Ridge Regression</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#ridge-complexity">3.1.2.1. Ridge Complexity</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#lasso">3.1.3. Lasso</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#elastic-net">3.1.4. Elastic Net</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#least-angle-regression">3.1.5. Least Angle Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#lars-lasso">3.1.6. LARS Lasso</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#mathematical-formulation">3.1.6.1. Mathematical formulation</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#bayesian-regression">3.1.7. Bayesian Regression</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#bayesian-ridge-regression">3.1.7.1. Bayesian Ridge Regression</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#automatic-relevance-determination-ard">3.1.8. Automatic Relevance Determination - ARD</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#id3">3.1.8.1. Mathematical formulation</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html#stochastic-gradient-descent-sgd">3.1.9. Stochastic Gradient Descent - SGD</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/svm.html">3.2. Support Vector Machines</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#classification">3.2.1. Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#regression">3.2.2. Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#density-estimation-outliers-detection">3.2.3. Density estimation, outliers detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#support-vector-machines-for-sparse-data">3.2.4. Support Vector machines for sparse data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#complexity">3.2.5. Complexity</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#tips-on-practical-use">3.2.6. Tips on Practical Use</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#kernel-functions">3.2.7. Kernel functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#custom-kernels">3.2.7.1. Custom Kernels</a><ul>
<li class="toctree-l4"><a class="reference internal" href="modules/svm.html#using-python-functions-as-kernels">3.2.7.1.1. Using python functions as kernels</a></li>
<li class="toctree-l4"><a class="reference internal" href="modules/svm.html#using-the-gram-matrix">3.2.7.1.2. Using the Gram matrix</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#mathematical-formulation">3.2.8. Mathematical formulation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#svc">3.2.8.1. SVC</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#nusvc">3.2.8.2. NuSVC</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#frequently-asked-questions">3.2.9. Frequently Asked Questions</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html#implementation-details">3.2.10. Implementation details</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/sgd.html">3.3. Stochastic Gradient Descent</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#classification">3.3.1. Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#regression">3.3.2. Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#stochastic-gradient-descent-for-sparse-data">3.3.3. Stochastic Gradient Descent for sparse data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#complexity">3.3.4. Complexity</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#tips-on-practical-use">3.3.5. Tips on Practical Use</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#mathematical-formulation">3.3.6. Mathematical formulation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/sgd.html#id1">3.3.6.1. SGD</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html#implementation-details">3.3.7. Implementation details</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/neighbors.html">3.4. Nearest Neighbors</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html#classification">3.4.1. Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html#regression">3.4.2. Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html#efficient-implementation-the-ball-tree">3.4.3. Efficient implementation: the ball tree</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/feature_selection.html">3.5. Feature selection</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html#univariate-feature-selection">3.5.1. Univariate feature selection</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/feature_selection.html#feature-scoring-functions">3.5.1.1. Feature scoring functions</a><ul>
<li class="toctree-l4"><a class="reference internal" href="modules/feature_selection.html#for-classification">3.5.1.1.1. For classification</a></li>
<li class="toctree-l4"><a class="reference internal" href="modules/feature_selection.html#for-regression">3.5.1.1.2. For regression</a></li>
</ul>
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</ul>
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<li class="toctree-l1"><a class="reference internal" href="modules/gaussian_process.html">3.6. Gaussian Processes</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#an-introductory-regression-example">3.6.1. An introductory regression example</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#mathematical-formulation">3.6.2. Mathematical formulation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/gaussian_process.html#the-initial-assumption">3.6.2.1. The initial assumption</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/gaussian_process.html#the-best-linear-unbiased-prediction-blup">3.6.2.2. 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">3.6.2.3. The empirical best linear unbiased predictor (EBLUP)</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#correlation-models">3.6.3. Correlation Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#regression-models">3.6.4. Regression Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html#implementation-details">3.6.5. Implementation details</a></li>
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