Computation times¶
00:15.991 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:07.044 |
0.0 MB |
Robust linear estimator fitting ( |
00:01.788 |
0.0 MB |
Lasso on dense and sparse data ( |
00:00.954 |
0.0 MB |
Lasso model selection: AIC-BIC / cross-validation ( |
00:00.701 |
0.0 MB |
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples ( |
00:00.653 |
0.0 MB |
Theil-Sen Regression ( |
00:00.502 |
0.0 MB |
Comparing Linear Bayesian Regressors ( |
00:00.497 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.413 |
0.0 MB |
Quantile regression ( |
00:00.372 |
0.0 MB |
Polynomial and Spline interpolation ( |
00:00.322 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.263 |
0.0 MB |
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent ( |
00:00.228 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.212 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.173 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.160 |
0.0 MB |
SGD: Penalties ( |
00:00.159 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.143 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.140 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.138 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.136 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.123 |
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Regularization path of L1- Logistic Regression ( |
00:00.091 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.080 |
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HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.075 |
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SGD: convex loss functions ( |
00:00.069 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.069 |
0.0 MB |
Lasso model selection via information criteria ( |
00:00.068 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.066 |
0.0 MB |
Logistic function ( |
00:00.058 |
0.0 MB |
Lasso path using LARS ( |
00:00.055 |
0.0 MB |
SGD: Weighted samples ( |
00:00.054 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.051 |
0.0 MB |
Non-negative least squares ( |
00:00.047 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.041 |
0.0 MB |
Linear Regression Example ( |
00:00.031 |
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 |