Computation times¶
00:20.684 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:08.263 |
0.0 MB |
Lasso on dense and sparse data ( |
00:02.059 |
0.0 MB |
Robust linear estimator fitting ( |
00:01.966 |
0.0 MB |
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples ( |
00:01.490 |
0.0 MB |
Lasso model selection: AIC-BIC / cross-validation ( |
00:00.775 |
0.0 MB |
Comparing Linear Bayesian Regressors ( |
00:00.620 |
0.0 MB |
Theil-Sen Regression ( |
00:00.601 |
0.0 MB |
Quantile regression ( |
00:00.501 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.487 |
0.0 MB |
Polynomial and Spline interpolation ( |
00:00.407 |
0.0 MB |
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent ( |
00:00.325 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.309 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.266 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.220 |
0.0 MB |
SGD: Penalties ( |
00:00.210 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.200 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.192 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.179 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.178 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.153 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.146 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.114 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.107 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.099 |
0.0 MB |
SGD: convex loss functions ( |
00:00.092 |
0.0 MB |
Lasso model selection via information criteria ( |
00:00.091 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.091 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.088 |
0.0 MB |
Logistic function ( |
00:00.078 |
0.0 MB |
Lasso path using LARS ( |
00:00.075 |
0.0 MB |
SGD: Weighted samples ( |
00:00.073 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.066 |
0.0 MB |
Non-negative least squares ( |
00:00.061 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.048 |
0.0 MB |
Linear Regression Example ( |
00:00.038 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:00.005 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.004 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.004 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.003 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.002 |
0.0 MB |