# Does it make sense to apply recursive feature elimination on one-hot encoded features?

Does it make sense to apply recursive feature elimination on a feature set pre-processed with One-Hot Encoding?

This is my code for feature selection:

xgb = XGBClassifier(n_estimators=100,
objective='multi:softprob',
num_class=4,
random_state=42)
rfecv = feature_selection.RFECV(estimator=xgb,
step=10,
cv=model_selection.StratifiedKFold(2),
scoring='f1_weighted',
n_jobs = -1,
verbose = 2)
rfecv.fit(X_train, y_train)


DataFrame X_train contains both continuous and categorical features. Categorical features are one-hot encoded, while continuous features are passed through MinMaxScaler.

I am not sure if it makes sense to eliminate one-hot encoded columns using RFECV. Maybe I should run RFECV on continuos features only? Or I should apply one-hot encoding somehow at each iteration of RFECV?