Computation times¶
00:42.842 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:21.962 |
0.0 MB |
Robust linear estimator fitting ( |
00:04.819 |
0.0 MB |
Lasso on dense and sparse data ( |
00:02.985 |
0.0 MB |
Lasso model selection: Cross-Validation / AIC / BIC ( |
00:02.026 |
0.0 MB |
Theil-Sen Regression ( |
00:01.548 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:01.183 |
0.0 MB |
Automatic Relevance Determination Regression (ARD) ( |
00:00.836 |
0.0 MB |
Bayesian Ridge Regression ( |
00:00.834 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.631 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.593 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.552 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.458 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.448 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.404 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.386 |
0.0 MB |
SGD: Penalties ( |
00:00.366 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.344 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.290 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.241 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.205 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.194 |
0.0 MB |
SGD: convex loss functions ( |
00:00.188 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.182 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.178 |
0.0 MB |
Logistic function ( |
00:00.167 |
0.0 MB |
Polynomial interpolation ( |
00:00.159 |
0.0 MB |
Lasso path using LARS ( |
00:00.154 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.135 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.130 |
0.0 MB |
SGD: Weighted samples ( |
00:00.119 |
0.0 MB |
Linear Regression Example ( |
00:00.081 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:00.010 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.009 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.009 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.007 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.006 |
0.0 MB |