Ridge Regression is the estimator used in this example. Each color in the left plot represents one different dimension of the coefficient vector, and this is displayed as a function of the regularization parameter. The right plot shows how exact the solution is. This example illustrates how a well defined solution is found by Ridge regression and how regularization affects the coefficients and their values. The plot on the right shows how the difference of the coefficients from the estimator c

A plot that compares the various convex loss functions supported by

Computes Lasso Path along the regularization parameter using the LARS algorithm on the diabetes dataset. Each color represents a different feature of the coefficient vector

Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encoded with a dictionary print(__doc__)

In this example we see how to robustly fit a linear model to faulty data using the RANSAC algorithm.

Fit regression model with Bayesian Ridge Regression. See

An example showing how different online solvers perform on the hand-written digits dataset.

The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected features to be the same across tasks. This example

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

Estimates Lasso and Elastic-Net regression models on a manually generated sparse signal corrupted with an additive noise. Estimated coefficients are