Computation times

00:19.272 total execution time for auto_examples_linear_model files:

Comparing various online solvers (plot_sgd_comparison.py)

00:07.694

0.0 MB

Lasso on dense and sparse data (plot_lasso_dense_vs_sparse_data.py)

00:01.969

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Robust linear estimator fitting (plot_robust_fit.py)

00:01.725

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Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples (plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py)

00:01.123

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Lasso model selection: AIC-BIC / cross-validation (plot_lasso_model_selection.py)

00:00.738

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One-Class SVM versus One-Class SVM using Stochastic Gradient Descent (plot_sgdocsvm_vs_ocsvm.py)

00:00.604

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Comparing Linear Bayesian Regressors (plot_ard.py)

00:00.561

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Theil-Sen Regression (plot_theilsen.py)

00:00.532

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Ridge coefficients as a function of the L2 Regularization (plot_ridge_coeffs.py)

00:00.494

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Quantile regression (plot_quantile_regression.py)

00:00.423

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L1 Penalty and Sparsity in Logistic Regression (plot_logistic_l1_l2_sparsity.py)

00:00.367

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Polynomial and Spline interpolation (plot_polynomial_interpolation.py)

00:00.340

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L1-based models for Sparse Signals (plot_lasso_and_elasticnet.py)

00:00.328

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Lasso and Elastic Net (plot_lasso_coordinate_descent_path.py)

00:00.234

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Joint feature selection with multi-task Lasso (plot_multi_task_lasso_support.py)

00:00.188

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SGD: Penalties (plot_sgd_penalties.py)

00:00.181

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Curve Fitting with Bayesian Ridge Regression (plot_bayesian_ridge_curvefit.py)

00:00.171

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Orthogonal Matching Pursuit (plot_omp.py)

00:00.165

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Ordinary Least Squares and Ridge Regression Variance (plot_ols_ridge_variance.py)

00:00.155

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Plot multinomial and One-vs-Rest Logistic Regression (plot_logistic_multinomial.py)

00:00.150

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Plot Ridge coefficients as a function of the regularization (plot_ridge_path.py)

00:00.139

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Sparsity Example: Fitting only features 1 and 2 (plot_ols_3d.py)

00:00.124

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Plot multi-class SGD on the iris dataset (plot_sgd_iris.py)

00:00.090

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HuberRegressor vs Ridge on dataset with strong outliers (plot_huber_vs_ridge.py)

00:00.086

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Regularization path of L1- Logistic Regression (plot_logistic_path.py)

00:00.079

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Lasso model selection via information criteria (plot_lasso_lars_ic.py)

00:00.078

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SGD: convex loss functions (plot_sgd_loss_functions.py)

00:00.076

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Robust linear model estimation using RANSAC (plot_ransac.py)

00:00.074

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Logistic function (plot_logistic.py)

00:00.068

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Lasso path using LARS (plot_lasso_lars.py)

00:00.063

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SGD: Weighted samples (plot_sgd_weighted_samples.py)

00:00.062

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SGD: Maximum margin separating hyperplane (plot_sgd_separating_hyperplane.py)

00:00.055

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Non-negative least squares (plot_nnls.py)

00:00.050

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Logistic Regression 3-class Classifier (plot_iris_logistic.py)

00:00.041

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Linear Regression Example (plot_ols.py)

00:00.031

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Tweedie regression on insurance claims (plot_tweedie_regression_insurance_claims.py)

00:00.005

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Multiclass sparse logistic regression on 20newgroups (plot_sparse_logistic_regression_20newsgroups.py)

00:00.004

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Early stopping of Stochastic Gradient Descent (plot_sgd_early_stopping.py)

00:00.004

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MNIST classification using multinomial logistic + L1 (plot_sparse_logistic_regression_mnist.py)

00:00.003

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Poisson regression and non-normal loss (plot_poisson_regression_non_normal_loss.py)

00:00.002

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