Computation times¶
00:21.868 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:08.201 |
0.0 MB |
Lasso on dense and sparse data ( |
00:01.891 |
0.0 MB |
Robust linear estimator fitting ( |
00:01.861 |
0.0 MB |
Quantile regression ( |
00:01.550 |
0.0 MB |
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples ( |
00:01.465 |
0.0 MB |
Lasso model selection: AIC-BIC / cross-validation ( |
00:00.792 |
0.0 MB |
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent ( |
00:00.585 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.574 |
0.0 MB |
Theil-Sen Regression ( |
00:00.572 |
0.0 MB |
Polynomial and Spline interpolation ( |
00:00.394 |
0.0 MB |
Automatic Relevance Determination Regression (ARD) ( |
00:00.391 |
0.0 MB |
Bayesian Ridge Regression ( |
00:00.384 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.285 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.262 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.236 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.206 |
0.0 MB |
SGD: Penalties ( |
00:00.205 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.188 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.182 |
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Sparsity Example: Fitting only features 1 and 2 ( |
00:00.174 |
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Ordinary Least Squares and Ridge Regression Variance ( |
00:00.160 |
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Plot Ridge coefficients as a function of the regularization ( |
00:00.128 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.112 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.108 |
0.0 MB |
Lasso model selection via information criteria ( |
00:00.096 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
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 ( |
00:00.087 |
0.0 MB |
Lasso path using LARS ( |
00:00.078 |
0.0 MB |
Logistic function ( |
00:00.072 |
0.0 MB |
SGD: Weighted samples ( |
00:00.069 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.067 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.066 |
0.0 MB |
Non-negative least squares ( |
00:00.062 |
0.0 MB |
Linear Regression Example ( |
00:00.047 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:00.015 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.012 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.008 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.006 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.005 |
0.0 MB |