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<div class="section" id="computation-times">
<span id="sphx-glr-auto-examples-linear-model-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
<p><strong>02:18.771</strong> total execution time for <strong>auto_examples_linear_model</strong> files:</p>
<ul class="simple">
<li><strong>00:55.339</strong>: <a class="reference internal" href="plot_sgd_early_stopping.html#sphx-glr-auto-examples-linear-model-plot-sgd-early-stopping-py"><span class="std std-ref">Early stopping of Stochastic Gradient Descent</span></a> (<code class="docutils literal"><span class="pre">plot_sgd_early_stopping.py</span></code>)</li>
<li><strong>00:25.488</strong>: <a class="reference internal" href="plot_sgd_comparison.html#sphx-glr-auto-examples-linear-model-plot-sgd-comparison-py"><span class="std std-ref">Comparing various online solvers</span></a> (<code class="docutils literal"><span class="pre">plot_sgd_comparison.py</span></code>)</li>
<li><strong>00:23.989</strong>: <a class="reference internal" href="plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py"><span class="std std-ref">MNIST classfification using multinomial logistic + L1</span></a> (<code class="docutils literal"><span class="pre">plot_sparse_logistic_regression_mnist.py</span></code>)</li>
<li><strong>00:11.897</strong>: <a class="reference internal" href="plot_sparse_logistic_regression_20newsgroups.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-20newsgroups-py"><span class="std std-ref">Multiclass sparse logisitic regression on newgroups20</span></a> (<code class="docutils literal"><span class="pre">plot_sparse_logistic_regression_20newsgroups.py</span></code>)</li>
<li><strong>00:05.577</strong>: <a class="reference internal" href="plot_robust_fit.html#sphx-glr-auto-examples-linear-model-plot-robust-fit-py"><span class="std std-ref">Robust linear estimator fitting</span></a> (<code class="docutils literal"><span class="pre">plot_robust_fit.py</span></code>)</li>
<li><strong>00:03.914</strong>: <a class="reference internal" href="plot_logistic_path.html#sphx-glr-auto-examples-linear-model-plot-logistic-path-py"><span class="std std-ref">Regularization path of L1- Logistic Regression</span></a> (<code class="docutils literal"><span class="pre">plot_logistic_path.py</span></code>)</li>
<li><strong>00:01.614</strong>: <a class="reference internal" href="plot_lasso_dense_vs_sparse_data.html#sphx-glr-auto-examples-linear-model-plot-lasso-dense-vs-sparse-data-py"><span class="std std-ref">Lasso on dense and sparse data</span></a> (<code class="docutils literal"><span class="pre">plot_lasso_dense_vs_sparse_data.py</span></code>)</li>
<li><strong>00:01.579</strong>: <a class="reference internal" href="plot_theilsen.html#sphx-glr-auto-examples-linear-model-plot-theilsen-py"><span class="std std-ref">Theil-Sen Regression</span></a> (<code class="docutils literal"><span class="pre">plot_theilsen.py</span></code>)</li>
<li><strong>00:01.339</strong>: <a class="reference internal" href="plot_lasso_model_selection.html#sphx-glr-auto-examples-linear-model-plot-lasso-model-selection-py"><span class="std std-ref">Lasso model selection: Cross-Validation / AIC / BIC</span></a> (<code class="docutils literal"><span class="pre">plot_lasso_model_selection.py</span></code>)</li>
<li><strong>00:00.791</strong>: <a class="reference internal" href="plot_ard.html#sphx-glr-auto-examples-linear-model-plot-ard-py"><span class="std std-ref">Automatic Relevance Determination Regression (ARD)</span></a> (<code class="docutils literal"><span class="pre">plot_ard.py</span></code>)</li>
<li><strong>00:00.781</strong>: <a class="reference internal" href="plot_logistic_l1_l2_sparsity.html#sphx-glr-auto-examples-linear-model-plot-logistic-l1-l2-sparsity-py"><span class="std std-ref">L1 Penalty and Sparsity in Logistic Regression</span></a> (<code class="docutils literal"><span class="pre">plot_logistic_l1_l2_sparsity.py</span></code>)</li>
<li><strong>00:00.636</strong>: <a class="reference internal" href="plot_omp.html#sphx-glr-auto-examples-linear-model-plot-omp-py"><span class="std std-ref">Orthogonal Matching Pursuit</span></a> (<code class="docutils literal"><span class="pre">plot_omp.py</span></code>)</li>
<li><strong>00:00.579</strong>: <a class="reference internal" href="plot_bayesian_ridge.html#sphx-glr-auto-examples-linear-model-plot-bayesian-ridge-py"><span class="std std-ref">Bayesian Ridge Regression</span></a> (<code class="docutils literal"><span class="pre">plot_bayesian_ridge.py</span></code>)</li>
<li><strong>00:00.518</strong>: <a class="reference internal" href="plot_logistic_multinomial.html#sphx-glr-auto-examples-linear-model-plot-logistic-multinomial-py"><span class="std std-ref">Plot multinomial and One-vs-Rest Logistic Regression</span></a> (<code class="docutils literal"><span class="pre">plot_logistic_multinomial.py</span></code>)</li>
<li><strong>00:00.514</strong>: <a class="reference internal" href="plot_lasso_coordinate_descent_path.html#sphx-glr-auto-examples-linear-model-plot-lasso-coordinate-descent-path-py"><span class="std std-ref">Lasso and Elastic Net</span></a> (<code class="docutils literal"><span class="pre">plot_lasso_coordinate_descent_path.py</span></code>)</li>
<li><strong>00:00.385</strong>: <a class="reference internal" href="plot_ridge_coeffs.html#sphx-glr-auto-examples-linear-model-plot-ridge-coeffs-py"><span class="std std-ref">Plot Ridge coefficients as a function of the L2 regularization</span></a> (<code class="docutils literal"><span class="pre">plot_ridge_coeffs.py</span></code>)</li>
<li><strong>00:00.371</strong>: <a class="reference internal" href="plot_sgd_penalties.html#sphx-glr-auto-examples-linear-model-plot-sgd-penalties-py"><span class="std std-ref">SGD: Penalties</span></a> (<code class="docutils literal"><span class="pre">plot_sgd_penalties.py</span></code>)</li>
<li><strong>00:00.330</strong>: <a class="reference internal" href="plot_ols_3d.html#sphx-glr-auto-examples-linear-model-plot-ols-3d-py"><span class="std std-ref">Sparsity Example: Fitting only features 1 and 2</span></a> (<code class="docutils literal"><span class="pre">plot_ols_3d.py</span></code>)</li>
<li><strong>00:00.307</strong>: <a class="reference internal" href="plot_sgd_iris.html#sphx-glr-auto-examples-linear-model-plot-sgd-iris-py"><span class="std std-ref">Plot multi-class SGD on the iris dataset</span></a> (<code class="docutils literal"><span class="pre">plot_sgd_iris.py</span></code>)</li>
<li><strong>00:00.285</strong>: <a class="reference internal" href="plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py"><span class="std std-ref">Robust linear model estimation using RANSAC</span></a> (<code class="docutils literal"><span class="pre">plot_ransac.py</span></code>)</li>
<li><strong>00:00.282</strong>: <a class="reference internal" href="plot_ols_ridge_variance.html#sphx-glr-auto-examples-linear-model-plot-ols-ridge-variance-py"><span class="std std-ref">Ordinary Least Squares and Ridge Regression Variance</span></a> (<code class="docutils literal"><span class="pre">plot_ols_ridge_variance.py</span></code>)</li>
<li><strong>00:00.276</strong>: <a class="reference internal" href="plot_multi_task_lasso_support.html#sphx-glr-auto-examples-linear-model-plot-multi-task-lasso-support-py"><span class="std std-ref">Joint feature selection with multi-task Lasso</span></a> (<code class="docutils literal"><span class="pre">plot_multi_task_lasso_support.py</span></code>)</li>
<li><strong>00:00.274</strong>: <a class="reference internal" href="plot_iris_logistic.html#sphx-glr-auto-examples-linear-model-plot-iris-logistic-py"><span class="std std-ref">Logistic Regression 3-class Classifier</span></a> (<code class="docutils literal"><span class="pre">plot_iris_logistic.py</span></code>)</li>
<li><strong>00:00.219</strong>: <a class="reference internal" href="plot_ridge_path.html#sphx-glr-auto-examples-linear-model-plot-ridge-path-py"><span class="std std-ref">Plot Ridge coefficients as a function of the regularization</span></a> (<code class="docutils literal"><span class="pre">plot_ridge_path.py</span></code>)</li>
<li><strong>00:00.199</strong>: <a class="reference internal" href="plot_sgd_weighted_samples.html#sphx-glr-auto-examples-linear-model-plot-sgd-weighted-samples-py"><span class="std std-ref">SGD: Weighted samples</span></a> (<code class="docutils literal"><span class="pre">plot_sgd_weighted_samples.py</span></code>)</li>
<li><strong>00:00.166</strong>: <a class="reference internal" href="plot_sgd_loss_functions.html#sphx-glr-auto-examples-linear-model-plot-sgd-loss-functions-py"><span class="std std-ref">SGD: convex loss functions</span></a> (<code class="docutils literal"><span class="pre">plot_sgd_loss_functions.py</span></code>)</li>
<li><strong>00:00.166</strong>: <a class="reference internal" href="plot_huber_vs_ridge.html#sphx-glr-auto-examples-linear-model-plot-huber-vs-ridge-py"><span class="std std-ref">HuberRegressor vs Ridge on dataset with strong outliers</span></a> (<code class="docutils literal"><span class="pre">plot_huber_vs_ridge.py</span></code>)</li>
<li><strong>00:00.164</strong>: <a class="reference internal" href="plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py"><span class="std std-ref">Linear Regression Example</span></a> (<code class="docutils literal"><span class="pre">plot_ols.py</span></code>)</li>
<li><strong>00:00.163</strong>: <a class="reference internal" href="plot_polynomial_interpolation.html#sphx-glr-auto-examples-linear-model-plot-polynomial-interpolation-py"><span class="std std-ref">Polynomial interpolation</span></a> (<code class="docutils literal"><span class="pre">plot_polynomial_interpolation.py</span></code>)</li>
<li><strong>00:00.162</strong>: <a class="reference internal" href="plot_lasso_lars.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-py"><span class="std std-ref">Lasso path using LARS</span></a> (<code class="docutils literal"><span class="pre">plot_lasso_lars.py</span></code>)</li>
<li><strong>00:00.158</strong>: <a class="reference internal" href="plot_logistic.html#sphx-glr-auto-examples-linear-model-plot-logistic-py"><span class="std std-ref">Logistic function</span></a> (<code class="docutils literal"><span class="pre">plot_logistic.py</span></code>)</li>
<li><strong>00:00.156</strong>: <a class="reference internal" href="plot_sgd_separating_hyperplane.html#sphx-glr-auto-examples-linear-model-plot-sgd-separating-hyperplane-py"><span class="std std-ref">SGD: Maximum margin separating hyperplane</span></a> (<code class="docutils literal"><span class="pre">plot_sgd_separating_hyperplane.py</span></code>)</li>
<li><strong>00:00.153</strong>: <a class="reference internal" href="plot_lasso_and_elasticnet.html#sphx-glr-auto-examples-linear-model-plot-lasso-and-elasticnet-py"><span class="std std-ref">Lasso and Elastic Net for Sparse Signals</span></a> (<code class="docutils literal"><span class="pre">plot_lasso_and_elasticnet.py</span></code>)</li>
</ul>
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