Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset. Plot the class probabilities

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

This example compares the timing of Birch (with and without the global clustering step) and MiniBatchKMeans on a synthetic dataset having 100,000 samples and

Datasets can often contain components of that require different feature extraction and processing pipelines. This scenario might occur when:

An illustration of Swiss Roll reduction with locally linear embedding

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

An example to illustrate multi-output regression with decision tree. The

The plots below illustrate the effect the parameter C has on the separation line. A large value of C basically tells our model that we do not have

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

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