Computation times¶
00:45.385 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:23.289 |
0.0 MB |
Robust linear estimator fitting ( |
00:05.077 |
0.0 MB |
Lasso on dense and sparse data ( |
00:03.171 |
0.0 MB |
Lasso model selection: Cross-Validation / AIC / BIC ( |
00:02.178 |
0.0 MB |
Theil-Sen Regression ( |
00:01.691 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:01.080 |
0.0 MB |
Automatic Relevance Determination Regression (ARD) ( |
00:00.894 |
0.0 MB |
Bayesian Ridge Regression ( |
00:00.890 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.645 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.643 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.590 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.479 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.479 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.447 |
0.0 MB |
SGD: Penalties ( |
00:00.437 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.394 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.371 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.306 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.246 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.219 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.211 |
0.0 MB |
SGD: convex loss functions ( |
00:00.206 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.196 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.186 |
0.0 MB |
Logistic function ( |
00:00.183 |
0.0 MB |
Polynomial interpolation ( |
00:00.170 |
0.0 MB |
Lasso path using LARS ( |
00:00.160 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.142 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.139 |
0.0 MB |
SGD: Weighted samples ( |
00:00.134 |
0.0 MB |
Linear Regression Example ( |
00:00.086 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:00.012 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.010 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.009 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.008 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.007 |
0.0 MB |