Computation times¶
00:19.272 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:07.694 |
0.0 MB |
Lasso on dense and sparse data ( |
00:01.969 |
0.0 MB |
Robust linear estimator fitting ( |
00:01.725 |
0.0 MB |
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples ( |
00:01.123 |
0.0 MB |
Lasso model selection: AIC-BIC / cross-validation ( |
00:00.738 |
0.0 MB |
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent ( |
00:00.604 |
0.0 MB |
Comparing Linear Bayesian Regressors ( |
00:00.561 |
0.0 MB |
Theil-Sen Regression ( |
00:00.532 |
0.0 MB |
Ridge coefficients as a function of the L2 Regularization ( |
00:00.494 |
0.0 MB |
Quantile regression ( |
00:00.423 |
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L1 Penalty and Sparsity in Logistic Regression ( |
00:00.367 |
0.0 MB |
Polynomial and Spline interpolation ( |
00:00.340 |
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L1-based models for Sparse Signals ( |
00:00.328 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.234 |
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Joint feature selection with multi-task Lasso ( |
00:00.188 |
0.0 MB |
SGD: Penalties ( |
00:00.181 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.171 |
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Orthogonal Matching Pursuit ( |
00:00.165 |
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Ordinary Least Squares and Ridge Regression Variance ( |
00:00.155 |
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Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.150 |
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Plot Ridge coefficients as a function of the regularization ( |
00:00.139 |
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Sparsity Example: Fitting only features 1 and 2 ( |
00:00.124 |
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Plot multi-class SGD on the iris dataset ( |
00:00.090 |
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HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.086 |
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Regularization path of L1- Logistic Regression ( |
00:00.079 |
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Lasso model selection via information criteria ( |
00:00.078 |
0.0 MB |
SGD: convex loss functions ( |
00:00.076 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.074 |
0.0 MB |
Logistic function ( |
00:00.068 |
0.0 MB |
Lasso path using LARS ( |
00:00.063 |
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SGD: Weighted samples ( |
00:00.062 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.055 |
0.0 MB |
Non-negative least squares ( |
00:00.050 |
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 ( |
00:00.005 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.004 |
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Early stopping of Stochastic Gradient Descent ( |
00:00.004 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.003 |
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Poisson regression and non-normal loss ( |
00:00.002 |
0.0 MB |