Computation times¶
00:16.121 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:06.773 |
0.0 MB |
Robust linear estimator fitting ( |
00:01.665 |
0.0 MB |
Lasso on dense and sparse data ( |
00:00.905 |
0.0 MB |
Quantile regression ( |
00:00.842 |
0.0 MB |
Lasso model selection: AIC-BIC / cross-validation ( |
00:00.722 |
0.0 MB |
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples ( |
00:00.613 |
0.0 MB |
Theil-Sen Regression ( |
00:00.503 |
0.0 MB |
Comparing Linear Bayesian Regressors ( |
00:00.502 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.418 |
0.0 MB |
Polynomial and Spline interpolation ( |
00:00.344 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.241 |
0.0 MB |
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent ( |
00:00.233 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.219 |
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Joint feature selection with multi-task Lasso ( |
00:00.195 |
0.0 MB |
SGD: Penalties ( |
00:00.172 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.160 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.152 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.150 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.140 |
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Sparsity Example: Fitting only features 1 and 2 ( |
00:00.137 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.116 |
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Plot multi-class SGD on the iris dataset ( |
00:00.086 |
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Regularization path of L1- Logistic Regression ( |
00:00.086 |
0.0 MB |
Lasso model selection via information criteria ( |
00:00.079 |
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HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.077 |
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Lasso and Elastic Net for Sparse Signals ( |
00:00.074 |
0.0 MB |
SGD: convex loss functions ( |
00:00.074 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.070 |
0.0 MB |
Lasso path using LARS ( |
00:00.061 |
0.0 MB |
Logistic function ( |
00:00.060 |
0.0 MB |
SGD: Weighted samples ( |
00:00.057 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.055 |
0.0 MB |
Non-negative least squares ( |
00:00.052 |
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 |
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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 |