sklearn.datasets.make_circles(n_samples=100, shuffle=True, noise=None, random_state=None, factor=0.8)

class sklearn.gaussian_process.kernels.WhiteKernel(noise_level=1.0, noise_level_bounds=(1e-05, 100000.0))

This example compares the timing of Birch (with and without the global clustering step) and MiniBatchKMeans on a synthetic dataset having 100,000 samples and

class sklearn.base.ClassifierMixin

class sklearn.cluster.bicluster.SpectralBiclustering(n_clusters=3, method='bistochastic', n_components=6, n_best=3,

Example builds a swiss roll dataset and runs hierarchical clustering on their position. For more information, see

Compare randomized search and grid search for optimizing hyperparameters of a random forest. All parameters that influence

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

Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset. Plot the class probabilities

An illustration of Swiss Roll reduction with locally linear embedding