sklearn.metrics.pairwise.paired_distances(X, Y, metric='euclidean', **kwds)

class sklearn.linear_model.RidgeClassifierCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None

class sklearn.feature_selection.GenericUnivariateSelect(score_func=, mode='percentile', param=1e-05)

class sklearn.cross_decomposition.CCA(n_components=2, scale=True, max_iter=500, tol=1e-06, copy=True)

sklearn.metrics.pairwise.polynomial_kernel(X, Y=None, degree=3, gamma=None, coef0=1)

class sklearn.linear_model.Lars(fit_intercept=True, verbose=False, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.2204460492503131e-16

sklearn.metrics.auc(x, y, reorder=False)

sklearn.linear_model.lars_path(X, y, Xy=None, Gram=None, max_iter=500, alpha_min=0, method='lar', copy_X=True, eps=2.2204460492503131e-16

class sklearn.preprocessing.MaxAbsScaler(copy=True)

class sklearn.gaussian_process.kernels.Exponentiation(kernel, exponent)