class sklearn.linear_model.OrthogonalMatchingPursuit(n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize=True

sklearn.cluster.ward_tree(X, connectivity=None, n_clusters=None, return_distance=False)

Section contents In this section, we introduce the

Hyper-parameters are parameters that are not directly learnt within estimators. In scikit-learn they are passed as arguments to the constructor of

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

class sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariances=False

This example studies the scalability profile of approximate 10-neighbors queries using the LSHForest with n_estimators=20 and

class sklearn.linear_model.OrthogonalMatchingPursuitCV(copy=True, fit_intercept=True, normalize=True, max_iter=None