Computation times¶
01:40.768 total execution time for auto_examples_linear_model files:
Early stopping of Stochastic Gradient Descent ( |
00:27.237 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:21.580 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:11.517 |
0.0 MB |
Comparing various online solvers ( |
00:11.087 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:08.431 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:05.872 |
0.0 MB |
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples ( |
00:02.378 |
0.0 MB |
Robust linear estimator fitting ( |
00:01.906 |
0.0 MB |
Lasso on dense and sparse data ( |
00:01.254 |
0.0 MB |
Lasso model selection: AIC-BIC / cross-validation ( |
00:01.091 |
0.0 MB |
Ridge coefficients as a function of the L2 Regularization ( |
00:00.703 |
0.0 MB |
Comparing Linear Bayesian Regressors ( |
00:00.683 |
0.0 MB |
L1-based models for Sparse Signals ( |
00:00.625 |
0.0 MB |
Theil-Sen Regression ( |
00:00.601 |
0.0 MB |
Quantile regression ( |
00:00.581 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.508 |
0.0 MB |
Polynomial and Spline interpolation ( |
00:00.469 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.464 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.377 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.368 |
0.0 MB |
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent ( |
00:00.355 |
0.0 MB |
SGD: Penalties ( |
00:00.296 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.295 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.280 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.211 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.210 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.205 |
0.0 MB |
Logistic function ( |
00:00.137 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.123 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.115 |
0.0 MB |
Lasso model selection via information criteria ( |
00:00.107 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.105 |
0.0 MB |
SGD: convex loss functions ( |
00:00.099 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.095 |
0.0 MB |
Lasso path using LARS ( |
00:00.089 |
0.0 MB |
SGD: Weighted samples ( |
00:00.078 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.071 |
0.0 MB |
Non-negative least squares ( |
00:00.070 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.058 |
0.0 MB |
Linear Regression Example ( |
00:00.038 |
0.0 MB |