This example demonstrates how to generate a checkerboard dataset and bicluster it using the Spectral Biclustering algorithm. The data is

sklearn.random_projection.johnson_lindenstrauss_min_dim(n_samples, eps=0.1)

Warning DEPRECATED

These figures aid in illustrating how a point cloud can be very flat in one direction?which is where PCA comes in to choose a direction that is not flat.

sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)

class sklearn.cluster.AgglomerativeClustering(n_clusters=2, affinity='euclidean', memory=Memory(cachedir=None), connectivity=None

Warning DEPRECATED class sklearn

Plot the contours of the three penalties. All of the above are supported by sklearn.linear_model.stochastic_gradient.

This is the class and function reference of scikit-learn. Please refer to the full user guide for further details, as the class and function raw specifications may

sklearn.neighbors.radius_neighbors_graph(X, radius, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False