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<li class="toctree-l1 has-children"><a class="reference internal" href="sklearn.decomposition.html">sklearn.decomposition</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">sklearn.linear_model</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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  <section id="module-sklearn.linear_model">
<span id="sklearn-linear-model"></span><span id="linear-model-ref"></span><h1>sklearn.linear_model<a class="headerlink" href="#module-sklearn.linear_model" title="Link to this heading">#</a></h1>
<p>A variety of linear models.</p>
<p><strong>User guide.</strong> See the <a class="reference internal" href="../modules/linear_model.html#linear-model"><span class="std std-ref">Linear Models</span></a> section for further details.</p>
<p>The following subsections are only rough guidelines: the same estimator can fall into multiple categories, depending on its parameters.</p>
<section id="linear-classifiers">
<span id="linear-model-ref-linear-classifiers"></span><h2>Linear classifiers<a class="headerlink" href="#linear-classifiers" title="Link to this heading">#</a></h2>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogisticRegression</span></code></a></p></td>
<td><p>Logistic Regression (aka logit, MaxEnt) classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a></p></td>
<td><p>Logistic Regression CV (aka logit, MaxEnt) classifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html#sklearn.linear_model.PassiveAggressiveClassifier" title="sklearn.linear_model.PassiveAggressiveClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PassiveAggressiveClassifier</span></code></a></p></td>
<td><p>Passive Aggressive Classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.Perceptron.html#sklearn.linear_model.Perceptron" title="sklearn.linear_model.Perceptron"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Perceptron</span></code></a></p></td>
<td><p>Linear perceptron classifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.RidgeClassifier.html#sklearn.linear_model.RidgeClassifier" title="sklearn.linear_model.RidgeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RidgeClassifier</span></code></a></p></td>
<td><p>Classifier using Ridge regression.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.RidgeClassifierCV.html#sklearn.linear_model.RidgeClassifierCV" title="sklearn.linear_model.RidgeClassifierCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RidgeClassifierCV</span></code></a></p></td>
<td><p>Ridge classifier with built-in cross-validation.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model.SGDClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SGDClassifier</span></code></a></p></td>
<td><p>Linear classifiers (SVM, logistic regression, etc.) with SGD training.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.SGDOneClassSVM.html#sklearn.linear_model.SGDOneClassSVM" title="sklearn.linear_model.SGDOneClassSVM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SGDOneClassSVM</span></code></a></p></td>
<td><p>Solves linear One-Class SVM using Stochastic Gradient Descent.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="classical-linear-regressors">
<span id="linear-model-ref-classical-linear-regressors"></span><h2>Classical linear regressors<a class="headerlink" href="#classical-linear-regressors" title="Link to this heading">#</a></h2>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression" title="sklearn.linear_model.LinearRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearRegression</span></code></a></p></td>
<td><p>Ordinary least squares Linear Regression.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge" title="sklearn.linear_model.Ridge"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Ridge</span></code></a></p></td>
<td><p>Linear least squares with l2 regularization.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.RidgeCV.html#sklearn.linear_model.RidgeCV" title="sklearn.linear_model.RidgeCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RidgeCV</span></code></a></p></td>
<td><p>Ridge regression with built-in cross-validation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model.SGDRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SGDRegressor</span></code></a></p></td>
<td><p>Linear model fitted by minimizing a regularized empirical loss with SGD.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="regressors-with-variable-selection">
<span id="linear-model-ref-regressors-with-variable-selection"></span><h2>Regressors with variable selection<a class="headerlink" href="#regressors-with-variable-selection" title="Link to this heading">#</a></h2>
<p>The following estimators have built-in variable selection fitting procedures, but any estimator using a L1 or elastic-net penalty also performs variable selection: typically <code class="xref py py-class docutils literal notranslate"><span class="pre">SGDRegressor</span></code> or <a class="reference internal" href="../modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model.SGDClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">SGDClassifier</span></code></a> with an appropriate penalty.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.ElasticNet.html#sklearn.linear_model.ElasticNet" title="sklearn.linear_model.ElasticNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ElasticNet</span></code></a></p></td>
<td><p>Linear regression with combined L1 and L2 priors as regularizer.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.ElasticNetCV.html#sklearn.linear_model.ElasticNetCV" title="sklearn.linear_model.ElasticNetCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ElasticNetCV</span></code></a></p></td>
<td><p>Elastic Net model with iterative fitting along a regularization path.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.Lars.html#sklearn.linear_model.Lars" title="sklearn.linear_model.Lars"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Lars</span></code></a></p></td>
<td><p>Least Angle Regression model a.k.a.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.LarsCV.html#sklearn.linear_model.LarsCV" title="sklearn.linear_model.LarsCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LarsCV</span></code></a></p></td>
<td><p>Cross-validated Least Angle Regression model.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.Lasso.html#sklearn.linear_model.Lasso" title="sklearn.linear_model.Lasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Lasso</span></code></a></p></td>
<td><p>Linear Model trained with L1 prior as regularizer (aka the Lasso).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.LassoCV.html#sklearn.linear_model.LassoCV" title="sklearn.linear_model.LassoCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LassoCV</span></code></a></p></td>
<td><p>Lasso linear model with iterative fitting along a regularization path.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.LassoLars.html#sklearn.linear_model.LassoLars" title="sklearn.linear_model.LassoLars"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LassoLars</span></code></a></p></td>
<td><p>Lasso model fit with Least Angle Regression a.k.a.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.LassoLarsCV.html#sklearn.linear_model.LassoLarsCV" title="sklearn.linear_model.LassoLarsCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LassoLarsCV</span></code></a></p></td>
<td><p>Cross-validated Lasso, using the LARS algorithm.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.LassoLarsIC.html#sklearn.linear_model.LassoLarsIC" title="sklearn.linear_model.LassoLarsIC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LassoLarsIC</span></code></a></p></td>
<td><p>Lasso model fit with Lars using BIC or AIC for model selection.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.OrthogonalMatchingPursuit.html#sklearn.linear_model.OrthogonalMatchingPursuit" title="sklearn.linear_model.OrthogonalMatchingPursuit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit</span></code></a></p></td>
<td><p>Orthogonal Matching Pursuit model (OMP).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html#sklearn.linear_model.OrthogonalMatchingPursuitCV" title="sklearn.linear_model.OrthogonalMatchingPursuitCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuitCV</span></code></a></p></td>
<td><p>Cross-validated Orthogonal Matching Pursuit model (OMP).</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="bayesian-regressors">
<span id="linear-model-ref-bayesian-regressors"></span><h2>Bayesian regressors<a class="headerlink" href="#bayesian-regressors" title="Link to this heading">#</a></h2>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.ARDRegression.html#sklearn.linear_model.ARDRegression" title="sklearn.linear_model.ARDRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ARDRegression</span></code></a></p></td>
<td><p>Bayesian ARD regression.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.BayesianRidge.html#sklearn.linear_model.BayesianRidge" title="sklearn.linear_model.BayesianRidge"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BayesianRidge</span></code></a></p></td>
<td><p>Bayesian ridge regression.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="multi-task-linear-regressors-with-variable-selection">
<span id="linear-model-ref-multi-task-linear-regressors-with-variable-selection"></span><h2>Multi-task linear regressors with variable selection<a class="headerlink" href="#multi-task-linear-regressors-with-variable-selection" title="Link to this heading">#</a></h2>
<p>These estimators fit multiple regression problems (or tasks) jointly, while inducing sparse coefficients. While the inferred coefficients may differ between the tasks, they are constrained to agree on the features that are selected (non-zero coefficients).</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.MultiTaskElasticNet.html#sklearn.linear_model.MultiTaskElasticNet" title="sklearn.linear_model.MultiTaskElasticNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiTaskElasticNet</span></code></a></p></td>
<td><p>Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.html#sklearn.linear_model.MultiTaskElasticNetCV" title="sklearn.linear_model.MultiTaskElasticNetCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiTaskElasticNetCV</span></code></a></p></td>
<td><p>Multi-task L1/L2 ElasticNet with built-in cross-validation.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.MultiTaskLasso.html#sklearn.linear_model.MultiTaskLasso" title="sklearn.linear_model.MultiTaskLasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiTaskLasso</span></code></a></p></td>
<td><p>Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.MultiTaskLassoCV.html#sklearn.linear_model.MultiTaskLassoCV" title="sklearn.linear_model.MultiTaskLassoCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiTaskLassoCV</span></code></a></p></td>
<td><p>Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="outlier-robust-regressors">
<span id="linear-model-ref-outlier-robust-regressors"></span><h2>Outlier-robust regressors<a class="headerlink" href="#outlier-robust-regressors" title="Link to this heading">#</a></h2>
<p>Any estimator using the Huber loss would also be robust to outliers, e.g., <code class="xref py py-class docutils literal notranslate"><span class="pre">SGDRegressor</span></code> with <code class="docutils literal notranslate"><span class="pre">loss='huber'</span></code>.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.HuberRegressor.html#sklearn.linear_model.HuberRegressor" title="sklearn.linear_model.HuberRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HuberRegressor</span></code></a></p></td>
<td><p>L2-regularized linear regression model that is robust to outliers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.QuantileRegressor.html#sklearn.linear_model.QuantileRegressor" title="sklearn.linear_model.QuantileRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantileRegressor</span></code></a></p></td>
<td><p>Linear regression model that predicts conditional quantiles.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.RANSACRegressor.html#sklearn.linear_model.RANSACRegressor" title="sklearn.linear_model.RANSACRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RANSACRegressor</span></code></a></p></td>
<td><p>RANSAC (RANdom SAmple Consensus) algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.TheilSenRegressor.html#sklearn.linear_model.TheilSenRegressor" title="sklearn.linear_model.TheilSenRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TheilSenRegressor</span></code></a></p></td>
<td><p>Theil-Sen Estimator: robust multivariate regression model.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="generalized-linear-models-glm-for-regression">
<span id="linear-model-ref-generalized-linear-models-glm-for-regression"></span><h2>Generalized linear models (GLM) for regression<a class="headerlink" href="#generalized-linear-models-glm-for-regression" title="Link to this heading">#</a></h2>
<p>These models allow for response variables to have error distributions other than a normal distribution.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.GammaRegressor.html#sklearn.linear_model.GammaRegressor" title="sklearn.linear_model.GammaRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GammaRegressor</span></code></a></p></td>
<td><p>Generalized Linear Model with a Gamma distribution.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.PoissonRegressor.html#sklearn.linear_model.PoissonRegressor" title="sklearn.linear_model.PoissonRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PoissonRegressor</span></code></a></p></td>
<td><p>Generalized Linear Model with a Poisson distribution.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.TweedieRegressor.html#sklearn.linear_model.TweedieRegressor" title="sklearn.linear_model.TweedieRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TweedieRegressor</span></code></a></p></td>
<td><p>Generalized Linear Model with a Tweedie distribution.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="miscellaneous">
<span id="linear-model-ref-miscellaneous"></span><h2>Miscellaneous<a class="headerlink" href="#miscellaneous" title="Link to this heading">#</a></h2>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html#sklearn.linear_model.PassiveAggressiveRegressor" title="sklearn.linear_model.PassiveAggressiveRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PassiveAggressiveRegressor</span></code></a></p></td>
<td><p>Passive Aggressive Regressor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.enet_path.html#sklearn.linear_model.enet_path" title="sklearn.linear_model.enet_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">enet_path</span></code></a></p></td>
<td><p>Compute elastic net path with coordinate descent.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.lars_path.html#sklearn.linear_model.lars_path" title="sklearn.linear_model.lars_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lars_path</span></code></a></p></td>
<td><p>Compute Least Angle Regression or Lasso path using the LARS algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.lars_path_gram.html#sklearn.linear_model.lars_path_gram" title="sklearn.linear_model.lars_path_gram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lars_path_gram</span></code></a></p></td>
<td><p>The lars_path in the sufficient stats mode.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.lasso_path.html#sklearn.linear_model.lasso_path" title="sklearn.linear_model.lasso_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lasso_path</span></code></a></p></td>
<td><p>Compute Lasso path with coordinate descent.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.orthogonal_mp.html#sklearn.linear_model.orthogonal_mp" title="sklearn.linear_model.orthogonal_mp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">orthogonal_mp</span></code></a></p></td>
<td><p>Orthogonal Matching Pursuit (OMP).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.orthogonal_mp_gram.html#sklearn.linear_model.orthogonal_mp_gram" title="sklearn.linear_model.orthogonal_mp_gram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">orthogonal_mp_gram</span></code></a></p></td>
<td><p>Gram Orthogonal Matching Pursuit (OMP).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.ridge_regression.html#sklearn.linear_model.ridge_regression" title="sklearn.linear_model.ridge_regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ridge_regression</span></code></a></p></td>
<td><p>Solve the ridge equation by the method of normal equations.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
</section>


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