Computation times¶
00:22.304 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:08.784 |
0.0 MB |
Lasso on dense and sparse data ( |
00:02.109 |
0.0 MB |
Robust linear estimator fitting ( |
00:02.011 |
0.0 MB |
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples ( |
00:01.256 |
0.0 MB |
Lasso model selection: AIC-BIC / cross-validation ( |
00:00.873 |
0.0 MB |
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent ( |
00:00.662 |
0.0 MB |
Comparing Linear Bayesian Regressors ( |
00:00.632 |
0.0 MB |
Theil-Sen Regression ( |
00:00.619 |
0.0 MB |
Ridge coefficients as a function of the L2 Regularization ( |
00:00.563 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.551 |
0.0 MB |
Quantile regression ( |
00:00.500 |
0.0 MB |
Polynomial and Spline interpolation ( |
00:00.400 |
0.0 MB |
L1-based models for Sparse Signals ( |
00:00.385 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.322 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.300 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.233 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.208 |
0.0 MB |
SGD: Penalties ( |
00:00.205 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.187 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.174 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.153 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.153 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.114 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.101 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.096 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.092 |
0.0 MB |
SGD: convex loss functions ( |
00:00.091 |
0.0 MB |
Lasso model selection via information criteria ( |
00:00.090 |
0.0 MB |
Logistic function ( |
00:00.076 |
0.0 MB |
Lasso path using LARS ( |
00:00.074 |
0.0 MB |
SGD: Weighted samples ( |
00:00.068 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.068 |
0.0 MB |
Non-negative least squares ( |
00:00.058 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.043 |
0.0 MB |
Linear Regression Example ( |
00:00.038 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:00.006 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.004 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.004 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.003 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.002 |
0.0 MB |