Computation times¶
00:32.195 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:17.712 |
0.0 MB |
Robust linear estimator fitting ( |
00:03.771 |
0.0 MB |
Lasso on dense and sparse data ( |
00:02.343 |
0.0 MB |
Theil-Sen Regression ( |
00:01.227 |
0.0 MB |
Lasso model selection: Cross-Validation / AIC / BIC ( |
00:00.943 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.792 |
0.0 MB |
Automatic Relevance Determination Regression (ARD) ( |
00:00.617 |
0.0 MB |
Bayesian Ridge Regression ( |
00:00.542 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.406 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.372 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.316 |
0.0 MB |
SGD: Penalties ( |
00:00.301 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.264 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.264 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.255 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.246 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.230 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.200 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.145 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.140 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.135 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.123 |
0.0 MB |
SGD: convex loss functions ( |
00:00.115 |
0.0 MB |
Lasso path using LARS ( |
00:00.103 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.102 |
0.0 MB |
Polynomial interpolation ( |
00:00.095 |
0.0 MB |
Logistic function ( |
00:00.094 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.093 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.089 |
0.0 MB |
SGD: Weighted samples ( |
00:00.087 |
0.0 MB |
Linear Regression Example ( |
00:00.051 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.009 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.007 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.006 |
0.0 MB |