sklearn.metrics.label_ranking_loss(y_true, y_score, sample_weight=None)

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

sklearn.datasets.make_regression(n_samples=100, n_features=100, n_informative=10, n_targets=1, bias=0.0, effective_rank=None, tail_strength=0

class sklearn.cluster.bicluster.SpectralBiclustering(n_clusters=3, method='bistochastic', n_components=6, n_best=3,

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

Warning DEPRECATED

sklearn.datasets.make_moons(n_samples=100, shuffle=True, noise=None, random_state=None)

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

Toy example of 1D regression using linear, polynomial and RBF kernels. print(__doc__)