How to retrieve column names from applying a wrapper method in feature selection? This question probably has a simple answer to it, so I will get to the point...
How do I retrieve the names of the columns from applying a wrapper method in feature selection?
Code I have used:
from mlxtend.feature_selection import SequentialFeatureSelector as SFS

X = df[['A','B','C','D']].values
y = df[['F']].values

classifier = KNeighborsClassifier(n_neighbors=7) 
code = SFS(classifier,
           k_features=5, 
           forward=True, 
           floating=False, 
           verbose=8,
           scoring='accuracy'
)

code.fit(X, y)
code.k_feature_names_ 

Output:
Features: 3/3 -- score: 0.78

('1' '2', '3')

Therefore, how do I retrieve the names of the features ('1' '2', '3')?
Based on this scikit-learn documentation, you can get a boolean mask (in the same order) of the input features, via the get_support method: