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

Warning DEPRECATED

sklearn.metrics.pairwise.linear_kernel(X, Y=None)

class sklearn.preprocessing.MultiLabelBinarizer(classes=None, sparse_output=False)

class sklearn.linear_model.Ridge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto',

Incremental principal component analysis (IPCA) is typically used as a replacement for principal component analysis (PCA) when the dataset to be decomposed is too large to fit

Linear Discriminant Analysis (

An example using a one-class SVM for novelty detection.

class sklearn.random_projection.SparseRandomProjection(n_components='auto', density='auto', eps=0.1, dense_output=False

sklearn.metrics.homogeneity_score(labels_true, labels_pred)