Computation times¶
00:27.790 total execution time for auto_examples_linear_model files:
Comparing various online solvers ( |
00:15.495 |
0.0 MB |
Robust linear estimator fitting ( |
00:02.421 |
0.0 MB |
Lasso on dense and sparse data ( |
00:02.186 |
0.0 MB |
Lasso model selection: Cross-Validation / AIC / BIC ( |
00:01.190 |
0.0 MB |
Theil-Sen Regression ( |
00:00.779 |
0.0 MB |
L1 Penalty and Sparsity in Logistic Regression ( |
00:00.680 |
0.0 MB |
Bayesian Ridge Regression ( |
00:00.508 |
0.0 MB |
Automatic Relevance Determination Regression (ARD) ( |
00:00.506 |
0.0 MB |
Plot Ridge coefficients as a function of the L2 regularization ( |
00:00.384 |
0.0 MB |
Lasso and Elastic Net ( |
00:00.339 |
0.0 MB |
Plot multinomial and One-vs-Rest Logistic Regression ( |
00:00.301 |
0.0 MB |
Joint feature selection with multi-task Lasso ( |
00:00.262 |
0.0 MB |
SGD: Penalties ( |
00:00.260 |
0.0 MB |
Curve Fitting with Bayesian Ridge Regression ( |
00:00.243 |
0.0 MB |
Ordinary Least Squares and Ridge Regression Variance ( |
00:00.234 |
0.0 MB |
Orthogonal Matching Pursuit ( |
00:00.232 |
0.0 MB |
Sparsity Example: Fitting only features 1 and 2 ( |
00:00.221 |
0.0 MB |
Plot Ridge coefficients as a function of the regularization ( |
00:00.182 |
0.0 MB |
Plot multi-class SGD on the iris dataset ( |
00:00.145 |
0.0 MB |
Regularization path of L1- Logistic Regression ( |
00:00.128 |
0.0 MB |
HuberRegressor vs Ridge on dataset with strong outliers ( |
00:00.119 |
0.0 MB |
Robust linear model estimation using RANSAC ( |
00:00.112 |
0.0 MB |
SGD: convex loss functions ( |
00:00.111 |
0.0 MB |
Lasso and Elastic Net for Sparse Signals ( |
00:00.107 |
0.0 MB |
Logistic function ( |
00:00.102 |
0.0 MB |
Polynomial interpolation ( |
00:00.100 |
0.0 MB |
Lasso path using LARS ( |
00:00.096 |
0.0 MB |
Logistic Regression 3-class Classifier ( |
00:00.090 |
0.0 MB |
SGD: Weighted samples ( |
00:00.087 |
0.0 MB |
SGD: Maximum margin separating hyperplane ( |
00:00.086 |
0.0 MB |
Linear Regression Example ( |
00:00.052 |
0.0 MB |
Tweedie regression on insurance claims ( |
00:00.009 |
0.0 MB |
MNIST classification using multinomial logistic + L1 ( |
00:00.007 |
0.0 MB |
Multiclass sparse logistic regression on 20newgroups ( |
00:00.007 |
0.0 MB |
Early stopping of Stochastic Gradient Descent ( |
00:00.006 |
0.0 MB |
Poisson regression and non-normal loss ( |
00:00.004 |
0.0 MB |