This example demonstrates the behavior of Gaussian mixture models fit on data that was not sampled from a mixture of Gaussian random variables. The dataset

Plot decision function of a weighted dataset, where the size of points is proportional to its weight. The sample weighting rescales the C parameter, which means

A tutorial exercise using Cross-validation with an SVM on the Digits dataset. This exercise is used in the

An illustration of dimensionality reduction on the S-curve dataset with various manifold learning methods. For a discussion and comparison of

Out-of-bag (OOB) estimates can be a useful heuristic to estimate the ?optimal? number of boosting iterations. OOB estimates are almost identical to cross-validation

A simple graphical frontend for Libsvm mainly intended for didactic purposes. You can create data points by point and click and visualize the decision region induced by different

Illustration of how the performance of an estimator on unseen data (test data) is not the same as the performance on training data. As the regularization increases

Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface and the support vectors.

Silhouette analysis can be used to study the separation distance between the resulting clusters. The silhouette

This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates the difference in performance between the discrete SAMME [2] boosting algorithm