Computation times¶

01:04.760 total execution time for auto_examples_linear_model files:

Comparing various online solvers (`plot_sgd_comparison.py`)	00:38.102	0.0 MB
Lasso on dense and sparse data (`plot_lasso_dense_vs_sparse_data.py`)	00:06.431	0.0 MB
Robust linear estimator fitting (`plot_robust_fit.py`)	00:04.733	0.0 MB
Lasso model selection: Cross-Validation / AIC / BIC (`plot_lasso_model_selection.py`)	00:02.408	0.0 MB
L1 Penalty and Sparsity in Logistic Regression (`plot_logistic_l1_l2_sparsity.py`)	00:01.573	0.0 MB
Theil-Sen Regression (`plot_theilsen.py`)	00:01.462	0.0 MB
Bayesian Ridge Regression (`plot_bayesian_ridge.py`)	00:01.017	0.0 MB
Automatic Relevance Determination Regression (ARD) (`plot_ard.py`)	00:01.003	0.0 MB
Plot Ridge coefficients as a function of the L2 regularization (`plot_ridge_coeffs.py`)	00:00.708	0.0 MB
Lasso and Elastic Net (`plot_lasso_coordinate_descent_path.py`)	00:00.674	0.0 MB
Plot multinomial and One-vs-Rest Logistic Regression (`plot_logistic_multinomial.py`)	00:00.621	0.0 MB
Joint feature selection with multi-task Lasso (`plot_multi_task_lasso_support.py`)	00:00.554	0.0 MB
SGD: Penalties (`plot_sgd_penalties.py`)	00:00.543	0.0 MB
Curve Fitting with Bayesian Ridge Regression (`plot_bayesian_ridge_curvefit.py`)	00:00.518	0.0 MB
Orthogonal Matching Pursuit (`plot_omp.py`)	00:00.514	0.0 MB
Sparsity Example: Fitting only features 1 and 2 (`plot_ols_3d.py`)	00:00.443	0.0 MB
Ordinary Least Squares and Ridge Regression Variance (`plot_ols_ridge_variance.py`)	00:00.441	0.0 MB
Plot Ridge coefficients as a function of the regularization (`plot_ridge_path.py`)	00:00.330	0.0 MB
Plot multi-class SGD on the iris dataset (`plot_sgd_iris.py`)	00:00.289	0.0 MB
Regularization path of L1- Logistic Regression (`plot_logistic_path.py`)	00:00.260	0.0 MB
SGD: convex loss functions (`plot_sgd_loss_functions.py`)	00:00.235	0.0 MB
HuberRegressor vs Ridge on dataset with strong outliers (`plot_huber_vs_ridge.py`)	00:00.230	0.0 MB
Lasso and Elastic Net for Sparse Signals (`plot_lasso_and_elasticnet.py`)	00:00.223	0.0 MB
Robust linear model estimation using RANSAC (`plot_ransac.py`)	00:00.213	0.0 MB
Polynomial interpolation (`plot_polynomial_interpolation.py`)	00:00.204	0.0 MB
Logistic function (`plot_logistic.py`)	00:00.191	0.0 MB
Lasso path using LARS (`plot_lasso_lars.py`)	00:00.182	0.0 MB
SGD: Weighted samples (`plot_sgd_weighted_samples.py`)	00:00.178	0.0 MB
Logistic Regression 3-class Classifier (`plot_iris_logistic.py`)	00:00.163	0.0 MB
SGD: Maximum margin separating hyperplane (`plot_sgd_separating_hyperplane.py`)	00:00.162	0.0 MB
Linear Regression Example (`plot_ols.py`)	00:00.099	0.0 MB
Tweedie regression on insurance claims (`plot_tweedie_regression_insurance_claims.py`)	00:00.015	0.0 MB
Early stopping of Stochastic Gradient Descent (`plot_sgd_early_stopping.py`)	00:00.012	0.0 MB
MNIST classification using multinomial logistic + L1 (`plot_sparse_logistic_regression_mnist.py`)	00:00.011	0.0 MB
Multiclass sparse logistic regression on 20newgroups (`plot_sparse_logistic_regression_20newsgroups.py`)	00:00.010	0.0 MB
Poisson regression and non-normal loss (`plot_poisson_regression_non_normal_loss.py`)	00:00.008	0.0 MB