class sklearn.kernel_approximation.RBFSampler(gamma=1.0, n_components=100, random_state=None)

class sklearn.linear_model.RidgeClassifier(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001

sklearn.cluster.affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200, damping=0.5, copy=True, verbose=False

sklearn.model_selection.train_test_split(*arrays, **options)

sklearn.metrics.pairwise.paired_euclidean_distances(X, Y)

A simple graphical frontend for Libsvm mainly intended for didactic purposes. You can create data points by point and click and visualize the decision region induced by different

sklearn.ensemble.partial_dependence.plot_partial_dependence(gbrt, X, features, feature_names=None, label=None

Sample usage of Nearest Neighbors classification. It will plot the decision boundaries for each class.

Find the optimal separating hyperplane using an SVC for classes that are unbalanced. We first find the separating plane with a plain

sklearn.covariance.ledoit_wolf(X, assume_centered=False, block_size=1000)