Computation timesΒΆ
03:27.210 total execution time for auto_examples_linear_model files:
01:18.534: Early stopping of Stochastic Gradient Descent (
plot_sgd_early_stopping.py
)00:45.020: MNIST classfification using multinomial logistic + L1 (
plot_sparse_logistic_regression_mnist.py
)00:29.265: Comparing various online solvers (
plot_sgd_comparison.py
)00:19.633: Multiclass sparse logisitic regression on newgroups20 (
plot_sparse_logistic_regression_20newsgroups.py
)00:07.138: Robust linear estimator fitting (
plot_robust_fit.py
)00:04.123: Regularization path of L1- Logistic Regression (
plot_logistic_path.py
)00:03.167: Automatic Relevance Determination Regression (ARD) (
plot_ard.py
)00:02.741: Lasso on dense and sparse data (
plot_lasso_dense_vs_sparse_data.py
)00:02.424: Theil-Sen Regression (
plot_theilsen.py
)00:02.306: Lasso model selection: Cross-Validation / AIC / BIC (
plot_lasso_model_selection.py
)00:01.528: L1 Penalty and Sparsity in Logistic Regression (
plot_logistic_l1_l2_sparsity.py
)00:01.245: Bayesian Ridge Regression (
plot_bayesian_ridge.py
)00:01.081: Orthogonal Matching Pursuit (
plot_omp.py
)00:00.946: Lasso and Elastic Net (
plot_lasso_coordinate_descent_path.py
)00:00.908: Plot Ridge coefficients as a function of the L2 regularization (
plot_ridge_coeffs.py
)00:00.724: Plot multinomial and One-vs-Rest Logistic Regression (
plot_logistic_multinomial.py
)00:00.668: SGD: Penalties (
plot_sgd_penalties.py
)00:00.594: Joint feature selection with multi-task Lasso (
plot_multi_task_lasso_support.py
)00:00.528: Sparsity Example: Fitting only features 1 and 2 (
plot_ols_3d.py
)00:00.485: Ordinary Least Squares and Ridge Regression Variance (
plot_ols_ridge_variance.py
)00:00.447: Plot Ridge coefficients as a function of the regularization (
plot_ridge_path.py
)00:00.447: Lasso and Elastic Net for Sparse Signals (
plot_lasso_and_elasticnet.py
)00:00.399: Plot multi-class SGD on the iris dataset (
plot_sgd_iris.py
)00:00.330: Lasso path using LARS (
plot_lasso_lars.py
)00:00.327: HuberRegressor vs Ridge on dataset with strong outliers (
plot_huber_vs_ridge.py
)00:00.312: SGD: convex loss functions (
plot_sgd_loss_functions.py
)00:00.286: Robust linear model estimation using RANSAC (
plot_ransac.py
)00:00.283: Linear Regression Example (
plot_ols.py
)00:00.281: SGD: Weighted samples (
plot_sgd_weighted_samples.py
)00:00.272: Polynomial interpolation (
plot_polynomial_interpolation.py
)00:00.264: SGD: Maximum margin separating hyperplane (
plot_sgd_separating_hyperplane.py
)00:00.262: Logistic Regression 3-class Classifier (
plot_iris_logistic.py
)00:00.239: Logistic function (
plot_logistic.py
)