Computation times¶
00:30.743 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:17.276 |
0.0 MB |
Robust linear estimator fitting ( |
00:02.654 |
0.0 MB |
Lasso on dense and sparse data ( |
00:02.549 |
0.0 MB |
Lasso model selection: Cross-Validation / AIC / BIC ( |
00:01.250 |
0.0 MB |
Theil-Sen Regression ( |
00:00.842 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.624 |
0.0 MB |
Bayesian Ridge Regression ( |
00:00.539 |
0.0 MB |
Automatic Relevance Determination Regression (ARD) ( |
00:00.537 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.387 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.386 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.364 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.342 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.316 |
0.0 MB |
SGD: Penalties ( |
00:00.280 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.258 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.251 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.236 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.183 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.178 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.133 |
0.0 MB |
SGD: convex loss functions ( |
00:00.126 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.125 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.121 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.118 |
0.0 MB |
Logistic function ( |
00:00.107 |
0.0 MB |
Polynomial interpolation ( |
00:00.106 |
0.0 MB |
Lasso path using LARS ( |
00:00.102 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.090 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.088 |
0.0 MB |
SGD: Weighted samples ( |
00:00.086 |
0.0 MB |
Linear Regression Example ( |
00:00.057 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:00.008 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.007 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.007 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.006 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.005 |
0.0 MB |