Computation times¶
00:28.362 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:15.506 |
0.0 MB |
Robust linear estimator fitting ( |
00:01.884 |
0.0 MB |
Lasso on dense and sparse data ( |
00:01.882 |
0.0 MB |
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples ( |
00:01.319 |
0.0 MB |
Quantile regression ( |
00:01.110 |
0.0 MB |
Lasso model selection: Cross-Validation / AIC / BIC ( |
00:00.807 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.580 |
0.0 MB |
Theil-Sen Regression ( |
00:00.576 |
0.0 MB |
Polynomial and Spline interpolation ( |
00:00.410 |
0.0 MB |
Automatic Relevance Determination Regression (ARD) ( |
00:00.401 |
0.0 MB |
Bayesian Ridge Regression ( |
00:00.395 |
0.0 MB |
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent ( |
00:00.322 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.290 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.268 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.241 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.217 |
0.0 MB |
SGD: Penalties ( |
00:00.208 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.189 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.189 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.180 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.165 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.130 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.114 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.110 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.095 |
0.0 MB |
SGD: convex loss functions ( |
00:00.094 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.090 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.090 |
0.0 MB |
Lasso path using LARS ( |
00:00.080 |
0.0 MB |
Logistic function ( |
00:00.075 |
0.0 MB |
SGD: Weighted samples ( |
00:00.071 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.069 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.068 |
0.0 MB |
Non-negative least squares ( |
00:00.064 |
0.0 MB |
Linear Regression Example ( |
00:00.048 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:00.008 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.006 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.005 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.005 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.004 |
0.0 MB |