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

The dataset used in this example is the 20 newsgroups dataset which will be automatically downloaded and then cached and reused for

Evaluate the ability of k-means initializations strategies to make the algorithm convergence robust as measured by the relative

When performing classification one often wants to predict not only the class label, but also the associated probability. This probability gives some kind of confidence

This example simulates a multi-label document classification problem. The dataset is generated randomly based on the following process: pick

This example shows that model selection can be performed with Gaussian Mixture Models using information-theoretic criteria (BIC). Model selection concerns both

Computes a Bayesian Ridge Regression on a synthetic dataset. See

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

The following example illustrates the effect of scaling the regularization parameter when using

Example of Precision-Recall metric to evaluate classifier output quality. In information retrieval, precision is a measure of result relevancy, while recall is a measure