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)

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

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

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

Section contents In this section, we introduce the

class sklearn.neighbors.BallTree BallTree for fast generalized N-point problems BallTree(X, leaf_size=40, metric=

class sklearn.multiclass.OneVsOneClassifier(estimator, n_jobs=1)

class sklearn.preprocessing.LabelEncoder