Computation times¶

00:47.965 total execution time for auto_examples_linear_model files:

Comparing various online solvers (`plot_sgd_comparison.py`)	00:25.097	0.0 MB
Robust linear estimator fitting (`plot_robust_fit.py`)	00:05.171	0.0 MB
Lasso on dense and sparse data (`plot_lasso_dense_vs_sparse_data.py`)	00:03.294	0.0 MB
Lasso model selection: Cross-Validation / AIC / BIC (`plot_lasso_model_selection.py`)	00:02.453	0.0 MB
Theil-Sen Regression (`plot_theilsen.py`)	00:01.804	0.0 MB
L1 Penalty and Sparsity in Logistic Regression (`plot_logistic_l1_l2_sparsity.py`)	00:01.089	0.0 MB
Bayesian Ridge Regression (`plot_bayesian_ridge.py`)	00:00.939	0.0 MB
Automatic Relevance Determination Regression (ARD) (`plot_ard.py`)	00:00.912	0.0 MB
Plot Ridge coefficients as a function of the L2 regularization (`plot_ridge_coeffs.py`)	00:00.653	0.0 MB
Lasso and Elastic Net (`plot_lasso_coordinate_descent_path.py`)	00:00.618	0.0 MB
Plot multinomial and One-vs-Rest Logistic Regression (`plot_logistic_multinomial.py`)	00:00.529	0.0 MB
Joint feature selection with multi-task Lasso (`plot_multi_task_lasso_support.py`)	00:00.501	0.0 MB
Curve Fitting with Bayesian Ridge Regression (`plot_bayesian_ridge_curvefit.py`)	00:00.476	0.0 MB
Ordinary Least Squares and Ridge Regression Variance (`plot_ols_ridge_variance.py`)	00:00.436	0.0 MB
Orthogonal Matching Pursuit (`plot_omp.py`)	00:00.433	0.0 MB
SGD: Penalties (`plot_sgd_penalties.py`)	00:00.427	0.0 MB
Sparsity Example: Fitting only features 1 and 2 (`plot_ols_3d.py`)	00:00.384	0.0 MB
Plot Ridge coefficients as a function of the regularization (`plot_ridge_path.py`)	00:00.322	0.0 MB
Plot multi-class SGD on the iris dataset (`plot_sgd_iris.py`)	00:00.272	0.0 MB
Regularization path of L1- Logistic Regression (`plot_logistic_path.py`)	00:00.220	0.0 MB
HuberRegressor vs Ridge on dataset with strong outliers (`plot_huber_vs_ridge.py`)	00:00.214	0.0 MB
SGD: convex loss functions (`plot_sgd_loss_functions.py`)	00:00.212	0.0 MB
Lasso and Elastic Net for Sparse Signals (`plot_lasso_and_elasticnet.py`)	00:00.201	0.0 MB
Robust linear model estimation using RANSAC (`plot_ransac.py`)	00:00.196	0.0 MB
Logistic function (`plot_logistic.py`)	00:00.177	0.0 MB
Polynomial interpolation (`plot_polynomial_interpolation.py`)	00:00.174	0.0 MB
Lasso path using LARS (`plot_lasso_lars.py`)	00:00.168	0.0 MB
SGD: Maximum margin separating hyperplane (`plot_sgd_separating_hyperplane.py`)	00:00.150	0.0 MB
Logistic Regression 3-class Classifier (`plot_iris_logistic.py`)	00:00.140	0.0 MB
SGD: Weighted samples (`plot_sgd_weighted_samples.py`)	00:00.128	0.0 MB
Linear Regression Example (`plot_ols.py`)	00:00.084	0.0 MB
MNIST classification using multinomial logistic + L1 (`plot_sparse_logistic_regression_mnist.py`)	00:00.025	0.0 MB
Poisson regression and non-normal loss (`plot_poisson_regression_non_normal_loss.py`)	00:00.020	0.0 MB
Multiclass sparse logistic regression on 20newgroups (`plot_sparse_logistic_regression_20newsgroups.py`)	00:00.017	0.0 MB
Early stopping of Stochastic Gradient Descent (`plot_sgd_early_stopping.py`)	00:00.015	0.0 MB
Tweedie regression on insurance claims (`plot_tweedie_regression_insurance_claims.py`)	00:00.014	0.0 MB