class sklearn.exceptions.DataConversionWarning

class sklearn.covariance.MinCovDet(store_precision=True, assume_centered=False, support_fraction=None, random_state=None)

class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)

class sklearn.svm.LinearSVR(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True

sklearn.covariance.empirical_covariance(X, assume_centered=False)

class sklearn.preprocessing.Imputer(missing_values='NaN', strategy='mean', axis=0, verbose=0, copy=True)

sklearn.linear_model.orthogonal_mp(X, y, n_nonzero_coefs=None, tol=None, precompute=False, copy_X=True, return_path=False, r

class sklearn.gaussian_process.kernels.ConstantKernel(constant_value=1.0, constant_value_bounds=(1e-05, 100000.0))

class sklearn.naive_bayes.MultinomialNB(alpha=1.0, fit_prior=True, class_prior=None)

sklearn.linear_model.lasso_stability_path(X, y, scaling=0.5, random_state=None, n_resampling=200, n_grid=100, sample_fraction=0