Plot the contours of the three penalties. All of the above are supported by sklearn.linear_model.stochastic_gradient.

Given a small number of observations, we want to recover which features of X are relevant to explain y. For this

Due to the few points in each dimension and the straight line that linear regression uses to follow these points as well as it can, noise

Plot decision surface of multinomial and One-vs-Rest Logistic Regression. The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers

Fit Ridge and HuberRegressor on a dataset with outliers. The example shows that the predictions in ridge are strongly influenced

Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented

Computes path on IRIS dataset. print(__doc__) # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>

Computes a Theil-Sen Regression on a synthetic dataset. See

Plot the maximum margin separating hyperplane within a two-class separable dataset using a linear Support Vector Machines classifier trained using SGD

Here a sine function is fit with a polynomial of order 3, for values close to zero. Robust fitting is demoed in different situations: No