class sklearn.covariance.GraphLassoCV(alphas=4, n_refinements=4, cv=None, tol=0.0001, enet_tol=0.0001, max_iter=100, mode='cd', n_jobs=1

class sklearn.feature_selection.GenericUnivariateSelect(score_func=, mode='percentile', param=1e-05)

sklearn.feature_extraction.image.grid_to_graph(n_x, n_y, n_z=1, mask=None, return_as=, dtype=)

This example demonstrates how to generate a dataset and bicluster it using the Spectral Co-Clustering algorithm. The dataset is generated

class sklearn.model_selection.PredefinedSplit(test_fold)

Computes a Theil-Sen Regression on a synthetic dataset. See

class sklearn.semi_supervised.LabelPropagation(kernel='rbf', gamma=20, n_neighbors=7, alpha=1, max_iter=30, tol=0.001, n_jobs=1)

sklearn.preprocessing.maxabs_scale(X, axis=0, copy=True)

sklearn.datasets.make_s_curve(n_samples=100, noise=0.0, random_state=None)

Shows how to use a function transformer in a pipeline. If you know your dataset?s first principle component is irrelevant for a classification task