4회 기출 유형(작업형2)의 xgb 에러 관련

선생님!! 4회 기출 유형(작업형2)에서 랜덤포레스트와 lgb 는 이상이 없는데, xgb 로 모델링 할 경우에만 에러가 발생하는데, 무슨 문제인가요?

[코딩]

import pandas as pd

train = pd.read_csv('train.csv')

test = pd.read_csv('test.csv')

# print(train.shape, test.shape)

# print(train.head(3))

# print(test.head(3))

# print(train.info())

# print(train.describe())

# print(train.describe(include='object'))

# print(test.describe(include='object'))

# print(train['Segmentation'].value_counts())

# print(train.isnull().sum())

# print(test.isnull().sum())

train = train.drop('ID', axis=1)

test_id = test.pop('ID')

test.head(3)

cols = train.select_dtypes(include='object').columns

print(cols)

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.head(3)

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=0.1, random_state=2022)

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

from sklearn.metrics import f1_score

from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier(random_state=2022, max_depth=7, n_estimators=100)

rf.fit(X_tr, y_tr)

pred = rf.predict(X_val)

pred[:10]

f1_score(y_val, pred, average='macro')

from sklearn.metrics import f1_score

# import lightgbm as lgb

# model = lgb.LGBMClassifier(random_state=2022, max_depth=5, n_estimators=800, learning_rate=0.01 )

# model.fit(X_tr, y_tr)

# pred = model.predict(X_val)

# pred[:10]

# f1_score(y_val, pred, average='macro')

from sklearn.metrics import f1_score

from xgboost import XGBClassifier

xgb = XGBClassifier(random_state=2022)

xgb.fit(X_tr, y_tr)

pred = xgb.predict(X_val)

pred[:10]

[에러내용]

ValueError Traceback (most recent call last)

<ipython-input-57-d656863c7bc3> in <cell line: 4>()

2 from xgboost import XGBClassifier

3 xgb = XGBClassifier(random_state=2022)

----> 4 xgb.fit(X_tr, y_tr)

5 pred = xgb.predict(X_val)

6 pred[:10]

1 frames

/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py in fit(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks)

1438 or not (self.classes_ == expected_classes).all()

1439 ):

-> 1440 raise ValueError(

1441 f"Invalid classes inferred from unique values of y. "

1442 f"Expected: {expected_classes}, got {self.classes_}"

ValueError: Invalid classes inferred from unique values of y. Expected: [0 1 2 3], got [1 2 3 4]