An example of estimating sources from noisy data.

Example of LabelPropagation learning a complex internal structure to demonstrate ?manifold learning?. The outer circle should be labeled ?red

Well calibrated classifiers are probabilistic classifiers for which the output of the predict_proba method can be directly interpreted as a confidence

Plot the confidence ellipsoids of a mixture of two Gaussians obtained with Expectation Maximisation (GaussianMixture class) and Variational Inference

Sometimes looking at the learned coefficients of a neural network can provide insight into the learning behavior. For example if weights look unstructured

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

An illustration of various embeddings on the digits dataset. The RandomTreesEmbedding, from the

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

This example demonstrates how to generate a checkerboard dataset and bicluster it using the Spectral Biclustering algorithm. The data is

Fit regression model with Bayesian Ridge Regression. See