The following plots demonstrate the impact of the number of clusters and number of samples on various clustering performance evaluation

Both kernel ridge regression (KRR) and Gaussian process regression (GPR) learn a target function by employing internally the ?kernel

A tutorial exercise regarding the use of classification techniques on the Digits dataset. This exercise is used in the

This example illustrates and compares the bias-variance decomposition of the expected mean squared error of a single estimator against

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

This example shows how kernel density estimation (KDE), a powerful non-parametric density estimation technique, can be used to learn a generative model for a dataset

In this example we compare the various initialization strategies for K-means in terms of runtime and quality of the results.

Shows how to use a function transformer in a pipeline. If you know your dataset?s first principle component is irrelevant for a classification task

An illustration of the metric and non-metric MDS on generated noisy data. The reconstructed points using the metric MDS and non metric MDS are slightly shifted

This example shows the effect of imposing a connectivity graph to capture local structure in the data. The graph is simply the graph of 20