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)
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