class sklearn.preprocessing.LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False)

sklearn.preprocessing.add_dummy_feature(X, value=1.0)

class sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True)

sklearn.preprocessing.maxabs_scale(X, axis=0, copy=True)

class sklearn.preprocessing.MaxAbsScaler(copy=True)

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

class sklearn.preprocessing.RobustScaler(with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)

class sklearn.preprocessing.Normalizer(norm='l2', copy=True)

class sklearn.preprocessing.OneHotEncoder(n_values='auto', categorical_features='all', dtype=, sparse=True, handle_unknown='error')

class sklearn.preprocessing.KernelCenterer