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

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

This submodule contains functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector machines (see

sklearn.metrics.fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average='binary', sample_weight=None)

sklearn.cluster.affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200, damping=0.5, copy=True, verbose=False

class sklearn.neighbors.NearestCentroid(metric='euclidean', shrink_threshold=None)

Warning DEPRECATED

sklearn.metrics.homogeneity_score(labels_true, labels_pred)

sklearn.covariance.ledoit_wolf(X, assume_centered=False, block_size=1000)

sklearn.svm.libsvm.cross_validation() Binding of the cross-validation routine (low-level routine)