ENH : Option to explicitly mention categorical columns during fit

[lashdk]

Description of Enhancement :
BaseModel during self._fetch_meta() selects categorical based on if the column dtype is 'object'. This is typically acceptable and would cover 95% of use cases but the inbuilt pandas dtype categorical would be missed by this . I propose we add categorical_feature parameter to our fit methods like in LGBM's fit method : categorical_feature (list of str or int, or 'auto', optional (default='auto'))(https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html#lightgbm.LGBMClassifier.fit)

How to reproduce:

from xplainable.core.models import XClassifier
from sklearn.datasets import make_classification
import numpy as np
X, y = make_classification(n_samples=1000,n_features=4,random_state=42,n_classes=2)
X = pd.DataFrame(X,columns=['Feat_'+str(i) for i in range(4)])
y = pd.Series(y)
X['Feat_0'] = X['Feat_0'].astype('category')
print(list(X.select_dtypes('object')))

Screenshot