Certainly, we can apply the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance in each group. Here's the updated code that includes SMOTE in each pipeline:
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from imblearn.over_sampling import SMOTE
import lightgbm as lgb

# Load the preprocessed training dataset
train_df = pd.read_csv('train.csv')

# Combine the original labels into new groups
group_0_df = train_df[train_df['label'].isin([0, 2])]  # Sci/Tech and Business
group_1_df = train_df[train_df['label'].isin([3, 4])]  # World and Politics
group_2_df = train_df[train_df['label'].isin([5, 6])]  # ESG and Health
group_3_df = train_df[train_df['label'].isin([1, 7])]  # Sports and Entertainment

# Define the machine learning pipeline for each group
pipeline_0 = Pipeline([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('smote', SMOTE(random_state=42)),
    ('lgbm', lgb.LGBMClassifier(n_estimators=1000))
])
pipeline_1 = Pipeline([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('smote', SMOTE(random_state=42)),
    ('lgbm', lgb.LGBMClassifier(n_estimators=1000))
])
pipeline_2 = Pipeline([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('smote', SMOTE(random_state=42)),
    ('lgbm', lgb.LGBMClassifier(n_estimators=1000))
])
pipeline_3 = Pipeline([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('smote', SMOTE(random_state=42)),
    ('lgbm', lgb.LGBMClassifier(n_estimators=1000))
])

# Train each model on its corresponding dataset
X_train_0, X_val_0, y_train_0, y_val_0 = train_test_split(group_0_df['text'], group_0_df['label'], test_size=0.2, random_state=42)
pipeline_0.fit(X_train_0, y_train_0)
y_pred_0 = pipeline_0.predict(X_val_0)
print(classification_report(y_val_0, y_pred_0))

X_train_1, X_val_1, y_train_1, y_val_1 = train_test_split(group_1_df['text'], group_1_df['label'], test_size=0.2, random_state=42)
pipeline_1.fit(X_train_1, y_train_1)
y_pred_1 = pipeline_1.predict(X_val_1)
print(classification_report(y_val_1, y_pred_1))

X_train_2, X_val_2, y_train_2, y_val_2 = train_test_split(group_2_df['text'], group_2_df['label'], test_size=0.2, random_state=42)
pipeline_2.fit(X_train_2, y_train_2)
y_pred_2 = pipeline_2.predict(X_val_2)
print(classification_report(y_val_2, y_pred_2))

X_train_3, X_val_3, y_train_3, y_val_3 = train_test_split(group_3_df['text'], group_3_df['label'], test_size=0.2, random_state
```
확실히 각 그룹의 클래스 불균형을 해결하기 위해 합성 소수 과대 샘플링 기법(SMOTE)을 적용할 수 있습니다. 다음은 각 파이프라인에 SMOTE를 포함하는 업데이트된 코드입니다:
'''python
import pandas as pd
sklearn.model_selection에서 train_test_split 가져오기
from sklearn.metrics import classification_report
sklearn.pipeline에서 파이프라인을 가져옵니다.
sklearn.feature_extraction.text에서 TfidfVectorizer를 가져옵니다.
imblearn.over_sampling에서 SMOTE를 가져옵니다.
LIGHTGBM을 LGB로 가져옵니다.

# 사전 처리된 훈련 데이터 세트 로드하기
train_df = pd.read_csv('train.csv')

# 원본 레이블을 새 그룹으로 결합합니다.
group_0_df = train_df[train_df['label'].isin([0, 2])]  # 과학/기술 및 비즈니스
group_1_df = train_df[train_df['label'].isin([3, 4])]  # 세계 및 정치
group_2_df = train_df[train_df['label'].isin([5, 6])]  # ESG 및 건강
group_3_df = train_df[train_df['label'].isin([1, 7])]  # 스포츠 및 엔터테인먼트

# 각 그룹에 대한 머신 러닝 파이프라인을 정의합니다.
pipeline_0 = 파이프라인([[
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('smote', SMOTE(random_state=42)),
    ('lgbm', lgb.LGBMClassifier(n_estimators=1000))
])
pipeline_1 = 파이프라인([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('smote', SMOTE(random_state=42)),
    ('lgbm', lgb.LGBMClassifier(n_estimators=1000))
])
pipeline_2 = 파이프라인([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('smote', SMOTE(random_state=42)),
    ('lgbm', lgb.LGBMClassifier(n_estimators=1000))
])
pipeline_3 = 파이프라인([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('smote', SMOTE(random_state=42)),
    ('lgbm', lgb.LGBMClassifier(n_estimators=1000))
])

# 각 모델을 해당 데이터 세트에서 훈련합니다.
X_train_0, X_val_0, y_train_0, y_val_0 = train_test_split(group_0_df['text'], group_0_df['label'], test_size=0.2, random_state=42)
pipeline_0.fit(X_train_0, y_train_0)
y_pred_0 = pipeline_0.predict(X_val_0)
print(classification_report(y_val_0, y_pred_0))

X_train_1, X_val_1, y_train_1, y_val_1 = train_test_split(group_1_df['text'], group_1_df['label'], test_size=0.2, random_state=42)
pipeline_1.fit(X_train_1, y_train_1)
y_pred_1 = pipeline_1.predict(X_val_1)
print(classification_report(y_val_1, y_pred_1))

X_train_2, X_val_2, y_train_2, y_val_2 = train_test_split(group_2_df['text'], group_2_df['label'], test_size=0.2, random_state=42)
pipeline_2.fit(X_train_2, y_train_2)
y_pred_2 = pipeline_2.predict(X_val_2)
print(classification_report(y_val_2, y_pred_2))

X_train_3, X_val_3, y_train_3, y_val_3 = train_test_split(group_3_df['text'], group_3_df['label'], test_size=0.2, random_state
```