This is an example showing how scikit-learn can be used for classification using an out-of-core approach: learning from data that doesn?t fit into

This example uses a large dataset of faces to learn a set of 20 x 20 images patches that constitute faces. From the programming standpoint

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

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

A plot that compares the various convex loss functions supported by

Find the optimal separating hyperplane using an SVC for classes that are unbalanced. We first find the separating plane with a plain

Using the GraphLasso estimator to learn a covariance and sparse precision from a small number of samples. To estimate a probabilistic model (e.g

This is an example of applying Non-negative Matrix Factorization and Latent Dirichlet Allocation on a

Making sure that each Feature has approximately the same scale can be a crucial preprocessing step. However, when data contains outliers,

This example plots the ellipsoids obtained from a toy dataset (mixture of three Gaussians) fitted by the Baye