watex.cases.prepare.default_pipeline#

watex.cases.prepare.default_pipeline(X, num_attributes, cat_attributes, y=None, label_encoding='LabelEncoder', **kws)[source]#

Default pipeline use for preprocessing the`Bagoue` dataset

The pipeline can be improved to achieve a good results.

Parameters:

X (ndarray, pd.DataFrame) – X or dataframe X

y: array_like,

ylabel or target

num_attributes:list

Numerical attributes

cat_attributes: list

categorical attributes

lableEncodage: str

Type of encoding used to encode the label Default is labelEncoder` but can be ``LabelBinarizer

Returns:

  • - `mum_pipeline` (Pipeline to process numerical features)

  • -`cat_pipeline` (pipeline to process categorical features.)

  • - `full_pipeline` (Full pipeline as the union of two pipelines)

  • -`y` (ylabel encoded if not None.)