Computation times¶
00:16.170 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:06.706 |
0.0 MB |
Robust linear estimator fitting ( |
00:01.697 |
0.0 MB |
Lasso on dense and sparse data ( |
00:00.904 |
0.0 MB |
Quantile regression ( |
00:00.838 |
0.0 MB |
Lasso model selection: AIC-BIC / cross-validation ( |
00:00.831 |
0.0 MB |
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples ( |
00:00.622 |
0.0 MB |
Comparing Linear Bayesian Regressors ( |
00:00.517 |
0.0 MB |
Theil-Sen Regression ( |
00:00.508 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.414 |
0.0 MB |
Polynomial and Spline interpolation ( |
00:00.324 |
0.0 MB |
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent ( |
00:00.241 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.236 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.217 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.174 |
0.0 MB |
SGD: Penalties ( |
00:00.172 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.161 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.149 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.147 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.144 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.139 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.108 |
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Plot multi-class SGD on the iris dataset ( |
00:00.087 |
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Regularization path of L1- Logistic Regression ( |
00:00.087 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.078 |
0.0 MB |
Lasso model selection via information criteria ( |
00:00.077 |
0.0 MB |
SGD: convex loss functions ( |
00:00.075 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.074 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.071 |
0.0 MB |
Lasso path using LARS ( |
00:00.061 |
0.0 MB |
Logistic function ( |
00:00.059 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.058 |
0.0 MB |
SGD: Weighted samples ( |
00:00.057 |
0.0 MB |
Non-negative least squares ( |
00:00.049 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.040 |
0.0 MB |
Linear Regression Example ( |
00:00.035 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:00.004 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.003 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.003 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.002 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.001 |
0.0 MB |