라벨인코딩을 했더니 F1 Score가 1.0이 나옵니다..

1. 실제 시험에서 너무 정확한 점수(높은)가 나오면 과적합을 의심해야 할까요?

   import pandas as pd
   
   # 다중분류 문제
   train = pd.read_csv("https://raw.githubusercontent.com/lovedlim/inf/refs/heads/main/p2/drug_train.csv")
   test = pd.read_csv("https://raw.githubusercontent.com/lovedlim/inf/refs/heads/main/p2/drug_test.csv")
   
   target = train.pop('Drug')
   
   # train, test 둘다 결측치 없고, 종류수 같음
   cols = train.select_dtypes(include='object').columns
   
   # baseline
   # train = train.drop(cols, axis=1)
   # test = test.drop(cols, axis=1)
   
   # one-hot
   # train = pd.get_dummies(train, columns = cols)
   # test = pd.get_dummies(test, columns = cols)
   
   # Label
   from sklearn.preprocessing import LabelEncoder
   for col in cols:
     le = LabelEncoder()
     train[col] = le.fit_transform(train[col])
     test[col] = le.transform(test[col])
   
   # 학습 데이터 분리
   from sklearn.model_selection import train_test_split
   X_tr, X_val, y_tr, y_val = train_test_split(
       train,
       target,
       test_size=0.2,
       random_state=0
   )
   
   # 모델 학습
   from sklearn.ensemble import RandomForestClassifier
   model = RandomForestClassifier(random_state=0)
   model.fit(X_tr, y_tr)
   pred = model.predict(X_val)
   
   # 모델 성능평가
   from sklearn.metrics import f1_score
   score = f1_score(y_val, pred, average='macro')
   print("F1 Score: ", score)
   
   # baseline F1 Score:  0.5535714285714286
   # one-hot  F1 Score:  0.8809523809523809
   # Label    F1 Score:  1.0
   
   # 모델 예측
   pred = model.predict(test)
   pd.DataFrame({
       'pred': pred
   }).to_csv('result.csv', index=False)
   
   pd.read_csv('result.csv')