Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the likelihood of new data can be used

Demonstrate the resolution of a regression problem using a k-Nearest Neighbor and the interpolation of the target using both barycenter and constant weights.

The usual covariance maximum likelihood estimate is very sensitive to the presence of outliers in the data set. In such a case, it would be better to

Example builds a swiss roll dataset and runs hierarchical clustering on their position. For more information, see

Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. The diagonal elements represent the number of points for which

Reference: Brendan J. Frey and Delbert Dueck, ?Clustering by Passing Messages Between Data Points?, Science Feb. 2007

An illustration of the isotonic regression on generated data. The isotonic regression finds a non-decreasing approximation of a function while minimizing the mean squared

This example illustrates the effect of the parameters gamma and C of the Radial Basis Function (RBF) kernel SVM. Intuitively, the gamma

An example illustrating the approximation of the feature map of an RBF kernel. It shows how to use

This example aims at showing characteristics of different clustering algorithms on datasets that are ?interesting? but still in 2D