인프런 커뮤니티 질문&답변

XGBClassifier 를 사용해서 target을 분류하려고 하는데, 아래와 같은 에러가 나타납니다 : ValueError: Invalid classes inferred from unique values of y. Expected: [0 1 2 3], got [1 2 3 4]

LabelEncoder를 사용해서 processing 도 다 했고, LGBMClassifier랑 RandomForestClassifier는 다 잘 돌아가는데 XGBClassifier만 저런 오류가 나타나네요;;; 참고를 위해 지금까지 작성한 코드 하기로 공유 드립니다 :

import pandas as pd
train=pd.read_csv('train.csv')
test=pd.read_csv('test.csv')

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

print(train.shape, test.shape)

print(train.head())
print(test.head())

print(train.info())
print(test.info())
print(train.isnull().sum())
print(test.isnull().sum())

y=train.pop('Segmentation')

y=y.astype('object')
y=y.astype('category')

print(y.info(), y.shape)

int_cols=train.select_dtypes(exclude='object').columns
train[int_cols].corr()

cat_cols=train.select_dtypes(include='object').columns
print(train[cat_cols].describe(include='object'))
print(test[cat_cols].describe(include='object'))

for i in cat_cols:
  train[i]=train[i].astype('object')
  test[i]=test[i].astype('object')

for i in cat_cols:
  print(train[i].value_counts())
  print(test[i].value_counts())


from sklearn.preprocessing import RobustScaler
scaler=RobustScaler()

for i in int_cols :
  train[i]=scaler.fit_transform(train[[i]])
  test[i]=scaler.transform(test[[i]])


from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()

for i in cat_cols:
  train[i]=le.fit_transform(train[i])
  test[i]=le.transform(test[i])

print(train.head())
print(test.head())

from sklearn.model_selection import train_test_split
X_tr, X_val, y_tr, y_val=train_test_split(train, y, test_size=0.2, random_state=2025)

print(X_tr.shape, X_val.shape, y_tr.shape, y_val.shape)

y_val=y_val.astype('category')
y_tr=y_tr.astype('category')

from sklearn.ensemble import RandomForestClassifier
rf=RandomForestClassifier(n_estimators=100, max_depth=5, random_state=2025)
rf.fit(X_tr, y_tr)
y_pred_rf=rf.predict(X_val)


from lightgbm import LGBMClassifier
lgbm=LGBMClassifier()
lgbm.fit(X_tr, y_tr)
y_pred_lgbm=lgbm.predict(X_val)


from sklearn.metrics import f1_score
print(f1_score(y_val, y_pred_rf, average='weighted'))
print(f1_score(y_val, y_pred_lgbm, average='weighted'))

from xgboost import XGBClassifier
xgb=XGBClassifier()
xgb.fit(X_tr, y_tr)
y_pred_xgb=xgb.predict(X_val)