Computation times¶
00:26.412 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:15.764 |
0.0 MB |
Robust linear estimator fitting ( |
00:02.069 |
0.0 MB |
Lasso on dense and sparse data ( |
00:02.069 |
0.0 MB |
Lasso model selection: Cross-Validation / AIC / BIC ( |
00:00.934 |
0.0 MB |
Theil-Sen Regression ( |
00:00.640 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.595 |
0.0 MB |
Bayesian Ridge Regression ( |
00:00.437 |
0.0 MB |
Automatic Relevance Determination Regression (ARD) ( |
00:00.434 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.315 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.294 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.253 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.239 |
0.0 MB |
SGD: Penalties ( |
00:00.223 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.220 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.195 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.195 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.185 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.144 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.124 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.120 |
0.0 MB |
SGD: convex loss functions ( |
00:00.108 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.104 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.099 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.099 |
0.0 MB |
Polynomial interpolation ( |
00:00.089 |
0.0 MB |
Lasso path using LARS ( |
00:00.087 |
0.0 MB |
Logistic function ( |
00:00.082 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.075 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.071 |
0.0 MB |
SGD: Weighted samples ( |
00:00.070 |
0.0 MB |
Linear Regression Example ( |
00:00.050 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:00.008 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.006 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.005 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.005 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.004 |
0.0 MB |