Why does train_final_model=True give different results compared to manually retraining? #2986

train_pool = Pool(X, y)
model = CatBoostClassifier(iterations=100, depth=7)
selection_result1 = model.select_features(train_pool,features_for_select=features_for_select,num_features_to_select=features_num,plot=False,train_final_model=True,algorithm="RecursiveByPredictionValuesChange")
feature_importance1 = model.get_feature_importance(prettified=True,type="PredictionValuesChange")
feature_importance1 = feature_importance1.iloc[0:features_num]
print(feature_importance1)

selection_result = model.select_features(train_pool,features_for_select=features_for_select,num_features_to_select=features_num,plot=False,train_final_model=False,algorithm="RecursiveByPredictionValuesChange")
X2 = X.iloc[:, selection_result['selected_features']]
train_pool2 = Pool(X2, y)
model = CatBoostClassifier(iterations=100, depth=7)
model.fit(train_pool2)
feature_importance = model.get_feature_importance(train_pool2,prettified=True,type="PredictionValuesChange")
print(feature_importance)