The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and Wolf proposed a close formula to compute the asymptotically optimal

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

This shows an example of a neighbors-based query (in particular a kernel density estimate) on geospatial data, using a Ball Tree built upon

This example illustrates the need for robust covariance estimation on a real data set. It is useful both for outlier detection and for a better understanding

This example shows how quantile regression can be used to create prediction intervals.

A recursive feature elimination example with automatic tuning of the number of features selected with cross-validation.

Simple usage of various cross decomposition algorithms: - PLSCanonical - PLSRegression, with multivariate response, a.k.a. PLS2 - PLSRegression, with univariate

An example to compare multi-output regression with random forest and the

Finds core samples of high density and expands clusters from them. print(__doc__) import numpy as np from