Computation times¶
00:18.044 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:06.841 |
0.0 MB |
Lasso on dense and sparse data ( |
00:02.025 |
0.0 MB |
Robust linear estimator fitting ( |
00:01.910 |
0.0 MB |
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples ( |
00:01.201 |
0.0 MB |
Lasso model selection: AIC-BIC / cross-validation ( |
00:00.688 |
0.0 MB |
Theil-Sen Regression ( |
00:00.604 |
0.0 MB |
Comparing Linear Bayesian Regressors ( |
00:00.538 |
0.0 MB |
Quantile regression ( |
00:00.449 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.382 |
0.0 MB |
Polynomial and Spline interpolation ( |
00:00.348 |
0.0 MB |
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent ( |
00:00.290 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.269 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.224 |
0.0 MB |
SGD: Penalties ( |
00:00.191 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.185 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.172 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.171 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.161 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.157 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.133 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.125 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.096 |
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Plot multi-class SGD on the iris dataset ( |
00:00.096 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.084 |
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SGD: convex loss functions ( |
00:00.080 |
0.0 MB |
Lasso model selection via information criteria ( |
00:00.077 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.077 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.076 |
0.0 MB |
Logistic function ( |
00:00.070 |
0.0 MB |
Lasso path using LARS ( |
00:00.065 |
0.0 MB |
SGD: Weighted samples ( |
00:00.062 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.056 |
0.0 MB |
Non-negative least squares ( |
00:00.052 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.042 |
0.0 MB |
Linear Regression Example ( |
00:00.032 |
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 ( |
00:00.003 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.002 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.002 |
0.0 MB |