Computation times¶
00:19.115 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:08.196 |
0.0 MB |
Robust linear estimator fitting ( |
00:01.969 |
0.0 MB |
Quantile regression ( |
00:01.074 |
0.0 MB |
Lasso on dense and sparse data ( |
00:00.953 |
0.0 MB |
Lasso model selection: AIC-BIC / cross-validation ( |
00:00.798 |
0.0 MB |
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples ( |
00:00.669 |
0.0 MB |
Comparing Linear Bayesian Regressors ( |
00:00.617 |
0.0 MB |
Theil-Sen Regression ( |
00:00.598 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.537 |
0.0 MB |
Polynomial and Spline interpolation ( |
00:00.389 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.284 |
0.0 MB |
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent ( |
00:00.283 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.256 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.204 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.189 |
0.0 MB |
SGD: Penalties ( |
00:00.188 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.174 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.170 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.164 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.155 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.129 |
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Regularization path of L1- Logistic Regression ( |
00:00.105 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.102 |
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HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.094 |
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Lasso model selection via information criteria ( |
00:00.094 |
0.0 MB |
SGD: convex loss functions ( |
00:00.091 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.087 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.086 |
0.0 MB |
Lasso path using LARS ( |
00:00.077 |
0.0 MB |
Logistic function ( |
00:00.073 |
0.0 MB |
SGD: Weighted samples ( |
00:00.071 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.065 |
0.0 MB |
Non-negative least squares ( |
00:00.062 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.048 |
0.0 MB |
Linear Regression Example ( |
00:00.044 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:00.005 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.004 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.004 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.003 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.002 |
0.0 MB |