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.)