Computation times

00:21.032 total execution time for auto_examples_linear_model files:

Comparing various online solvers (plot_sgd_comparison.py)	00:08.147	0.0 MB
Lasso on dense and sparse data (plot_lasso_dense_vs_sparse_data.py)	00:02.083	0.0 MB
Robust linear estimator fitting (plot_robust_fit.py)	00:01.924	0.0 MB
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples (plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py)	00:01.466	0.0 MB
Quantile regression (plot_quantile_regression.py)	00:01.114	0.0 MB
Lasso model selection: AIC-BIC / cross-validation (plot_lasso_model_selection.py)	00:00.798	0.0 MB
Comparing Linear Bayesian Regressors (plot_ard.py)	00:00.626	0.0 MB
Theil-Sen Regression (plot_theilsen.py)	00:00.595	0.0 MB
L1 Penalty and Sparsity in Logistic Regression (plot_logistic_l1_l2_sparsity.py)	00:00.534	0.0 MB
Polynomial and Spline interpolation (plot_polynomial_interpolation.py)	00:00.388	0.0 MB
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent (plot_sgdocsvm_vs_ocsvm.py)	00:00.316	0.0 MB
Plot Ridge coefficients as a function of the L2 regularization (plot_ridge_coeffs.py)	00:00.279	0.0 MB
Lasso and Elastic Net (plot_lasso_coordinate_descent_path.py)	00:00.254	0.0 MB
Joint feature selection with multi-task Lasso (plot_multi_task_lasso_support.py)	00:00.206	0.0 MB
SGD: Penalties (plot_sgd_penalties.py)	00:00.203	0.0 MB
Curve Fitting with Bayesian Ridge Regression (plot_bayesian_ridge_curvefit.py)	00:00.186	0.0 MB
Orthogonal Matching Pursuit (plot_omp.py)	00:00.181	0.0 MB
Plot multinomial and One-vs-Rest Logistic Regression (plot_logistic_multinomial.py)	00:00.170	0.0 MB
Ordinary Least Squares and Ridge Regression Variance (plot_ols_ridge_variance.py)	00:00.159	0.0 MB
Sparsity Example: Fitting only features 1 and 2 (plot_ols_3d.py)	00:00.157	0.0 MB
Plot Ridge coefficients as a function of the regularization (plot_ridge_path.py)	00:00.127	0.0 MB
Regularization path of L1- Logistic Regression (plot_logistic_path.py)	00:00.106	0.0 MB
Plot multi-class SGD on the iris dataset (plot_sgd_iris.py)	00:00.102	0.0 MB
HuberRegressor vs Ridge on dataset with strong outliers (plot_huber_vs_ridge.py)	00:00.094	0.0 MB
Lasso model selection via information criteria (plot_lasso_lars_ic.py)	00:00.093	0.0 MB
SGD: convex loss functions (plot_sgd_loss_functions.py)	00:00.090	0.0 MB
Lasso and Elastic Net for Sparse Signals (plot_lasso_and_elasticnet.py)	00:00.088	0.0 MB
Robust linear model estimation using RANSAC (plot_ransac.py)	00:00.086	0.0 MB
Lasso path using LARS (plot_lasso_lars.py)	00:00.077	0.0 MB
Logistic function (plot_logistic.py)	00:00.072	0.0 MB
SGD: Weighted samples (plot_sgd_weighted_samples.py)	00:00.071	0.0 MB
SGD: Maximum margin separating hyperplane (plot_sgd_separating_hyperplane.py)	00:00.066	0.0 MB
Non-negative least squares (plot_nnls.py)	00:00.062	0.0 MB
Logistic Regression 3-class Classifier (plot_iris_logistic.py)	00:00.049	0.0 MB
Linear Regression Example (plot_ols.py)	00:00.045	0.0 MB
Tweedie regression on insurance claims (plot_tweedie_regression_insurance_claims.py)	00:00.005	0.0 MB
Multiclass sparse logistic regression on 20newgroups (plot_sparse_logistic_regression_20newsgroups.py)	00:00.004	0.0 MB
Early stopping of Stochastic Gradient Descent (plot_sgd_early_stopping.py)	00:00.004	0.0 MB
MNIST classification using multinomial logistic + L1 (plot_sparse_logistic_regression_mnist.py)	00:00.003	0.0 MB
Poisson regression and non-normal loss (plot_poisson_regression_non_normal_loss.py)	00:00.002	0.0 MB