class sklearn.cross_decomposition.CCA(n_components=2, scale=True, max_iter=500, tol=1e-06, copy=True)

This example shows how kernel density estimation (KDE), a powerful non-parametric density estimation technique, can be used to learn a generative model for a dataset

sklearn.datasets.dump_svmlight_file(X, y, f, zero_based=True, comment=None, query_id=None, multilabel=False)

This example shows the effect of imposing a connectivity graph to capture local structure in the data. The graph is simply the graph of 20

sklearn.model_selection.validation_curve(estimator, X, y, param_name, param_range, groups=None, cv=None, scoring=None,

class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, precompute='auto', max_iter=1000

sklearn.datasets.make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True, random_state=None)

class sklearn.gaussian_process.kernels.Product(k1, k2)

class sklearn.decomposition.ProjectedGradientNMF(*args, **kwargs)

sklearn.metrics.adjusted_mutual_info_score(labels_true, labels_pred)