4회 기출 유형(작업형2) 질문

안녕하세요 강사님 아래와 같이 코드를 짜보고 강의를 들었는데

1. 저는 train_test_split을 썼으므로 강의에서와 같이

target = train.pop('Segmentation') 과정이 필요없는게 맞나요?

2. 아래는 제가 짠 코드이고 leaderboard 점수 0.30927입니다. 이정도 점수면 충분할까요?

3. 제가 아래 코드에

from sklearn.metrics import roc_auc_score 
print(roc_auc_score(y_val, pred) 

를 추가할시 아래와 같은 에러코드가 납니다. 자체적으로 평가점수를 보고싶어서 추가해보았는데 왜 이런문제가 생기는걸까요?

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_27/2182156644.py in <module>
     28 
     29 from sklearn.metrics import roc_auc_score
---> 30 print(roc_auc_score(y_val, pred))
     31 
     32 

/opt/conda/lib/python3.7/site-packages/sklearn/metrics/_ranking.py in roc_auc_score(y_true, y_score, average, sample_weight, max_fpr, multi_class, labels)
    558             )
    559         if multi_class == "raise":
--> 560             raise ValueError("multi_class must be in ('ovo', 'ovr')")
    561         return _multiclass_roc_auc_score(
    562             y_true, y_score, labels, multi_class, average, sample_weight

ValueError: multi_class must be in ('ovo', 'ovr')

import pandas as pd
train = pd.read_csv("../input/big-data-analytics-certification-kr-2022/train.csv")
test = pd.read_csv("../input/big-data-analytics-certification-kr-2022/test.csv")

cols = ['Gender', 'Ever_Married', 'Graduated', 'Profession', 'Spending_Score', 'Var_1']
from sklearn.preprocessing import LabelEncoder
for col in cols:
    le = LabelEncoder()
    train[col] = le.fit_transform(train[col])
    test[col] = le.transform(test[col])

train = train.drop('ID', axis =1)
test_id = test.pop('ID')

from sklearn.model_selection import train_test_split

X_tr, X_val, y_tr, y_val = train_test_split(
                                           train.drop(['Segmentation'],axis =1),
                                           train['Segmentation'],
                                           test_size = 0.2,
                                           random_state = 2023)

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_tr, y_tr)
pred = model.predict(X_val)


pred = model.predict(test)
 
submit = pd.DataFrame({
                        'ID': test_id,
                        'Segmentation' : pred })
submit.to_csv("submission.csv", index = False)