sklearn.metrics.pairwise.paired_distances(X, Y, metric='euclidean', **kwds)

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

This example illustrates and compares the bias-variance decomposition of the expected mean squared error of a single estimator against

sklearn.datasets.fetch_rcv1(data_home=None, subset='all', download_if_missing=True, random_state=None, shuffle=False)

A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D sinusoidal dataset with a small amount of Gaussian noise. 299 boosts (300 decision

class sklearn.pipeline.FeatureUnion(transformer_list, n_jobs=1, transformer_weights=None)

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

class sklearn.preprocessing.MaxAbsScaler(copy=True)

class sklearn.covariance.EmpiricalCovariance(store_precision=True, assume_centered=False)

class sklearn.linear_model.MultiTaskLassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, max_iter=1000