class sklearn.linear_model.Ridge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto',
class sklearn.linear_model.TheilSenRegressor(fit_intercept=True, copy_X=True, max_subpopulation=10000.0, n_subsamples=None,
class sklearn.kernel_ridge.KernelRidge(alpha=1, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None)
class sklearn.ensemble.ExtraTreesRegressor(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1
sklearn.metrics.pairwise.paired_euclidean_distances(X, Y)
class sklearn.random_projection.SparseRandomProjection(n_components='auto', density='auto', eps=0.1, dense_output=False
class sklearn.ensemble.GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0,
class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski'
class sklearn.linear_model.HuberRegressor(epsilon=1.35, max_iter=100, alpha=0.0001, warm_start=False, fit_intercept=True, tol=1e-05)
class sklearn.neighbors.BallTree BallTree for fast generalized N-point problems BallTree(X, leaf_size=40, metric=
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