인프런 커뮤니티 질문&답변

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')