Computation times¶
00:18.587 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:07.967 |
0.0 MB |
Robust linear estimator fitting ( |
00:01.952 |
0.0 MB |
Lasso on dense and sparse data ( |
00:00.958 |
0.0 MB |
Quantile regression ( |
00:00.873 |
0.0 MB |
Lasso model selection: AIC-BIC / cross-validation ( |
00:00.807 |
0.0 MB |
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples ( |
00:00.599 |
0.0 MB |
Theil-Sen Regression ( |
00:00.598 |
0.0 MB |
Comparing Linear Bayesian Regressors ( |
00:00.566 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.484 |
0.0 MB |
Polynomial and Spline interpolation ( |
00:00.432 |
0.0 MB |
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent ( |
00:00.281 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.274 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.260 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.212 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.203 |
0.0 MB |
SGD: Penalties ( |
00:00.197 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.177 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.177 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.175 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.147 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.121 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.113 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.104 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.093 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.090 |
0.0 MB |
SGD: convex loss functions ( |
00:00.089 |
0.0 MB |
Lasso model selection via information criteria ( |
00:00.087 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.087 |
0.0 MB |
Lasso path using LARS ( |
00:00.084 |
0.0 MB |
Logistic function ( |
00:00.076 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.071 |
0.0 MB |
SGD: Weighted samples ( |
00:00.068 |
0.0 MB |
Non-negative least squares ( |
00:00.062 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.046 |
0.0 MB |
Linear Regression Example ( |
00:00.041 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:00.004 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.004 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.003 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.002 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.001 |
0.0 MB |