An example showing univariate feature selection. Noisy (non informative) features are added to the iris data and univariate feature selection is applied

Given a small number of observations, we want to recover which features of X are relevant to explain y. For this

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

When performing classification you often want to predict not only the class label, but also the associated probability. This probability gives you some

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

Shows how shrinkage improves classification.

This example demonstrates how to generate a dataset and bicluster it using the Spectral Co-Clustering algorithm. The dataset is generated

This example demonstrates the behaviour of the accuracy of the nearest neighbor queries of Locality Sensitive Hashing Forest as the number

Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the

This example reproduces Figure 1 of Zhu et al [1] and shows how boosting can improve prediction accuracy on a multi-class problem. The classification dataset