class sklearn.preprocessing.LabelEncoder

sklearn.preprocessing.scale(X, axis=0, with_mean=True, with_std=True, copy=True)

class sklearn.linear_model.SGDRegressor(loss='squared_loss', penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, n_iter=5

class sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None)

sklearn.metrics.pairwise.linear_kernel(X, Y=None)

sklearn.datasets.make_friedman2(n_samples=100, noise=0.0, random_state=None)

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

class sklearn.linear_model.TheilSenRegressor(fit_intercept=True, copy_X=True, max_subpopulation=10000.0, n_subsamples=None,

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

class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski'