Thank you for the clarification. I apologize for any confusion I may have caused earlier. Based on your latest update, it seems that you would like to combine some of the original labels into new groups for training the models. Here's the updated code to reflect this:
```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 sklearn.linear_model import LogisticRegression

# 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)),
    ('clf', LogisticRegression(max_iter=1000))
])
pipeline_1 = Pipeline([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('clf', LogisticRegression(max_iter=1000))
])
pipeline_2 = Pipeline([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('clf', LogisticRegression(max_iter=1000))
])
pipeline_3 = Pipeline([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('clf', LogisticRegression(max_iter=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=42)
pipeline_3.fit(X_train_3, y_train_3)
y_pred_3 = pipeline_3.predict(X_val_3)
print(classification_report(y_val_3, y_pred_3))

# Load the test dataset
test_df = pd.read_csv('test.csv')

# Make predictions for each
```
명확히 설명해 주셔서 감사합니다. 이전에 혼란을 드린 점 사과드립니다. 최근 업데이트에 따르면 모델 학습을 위해 원래 레이블 중 일부를 새 그룹으로 결합하고 싶으신 것 같습니다. 이를 반영하기 위해 업데이트된 코드는 다음과 같습니다:
'``python
임포트 판다를 PD로
sklearn.model_selection에서 train_test_split 가져오기
from sklearn.metrics import classification_report
sklearn.pipeline에서 파이프라인을 가져옵니다.
sklearn.feature_extraction.text에서 TfidfVectorizer를 가져옵니다.
sklearn.linear_model에서 로지스틱 회귀를 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)),
    ('clf', LogisticRegression(max_iter=1000))
])
pipeline_1 = 파이프라인([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('clf', LogisticRegression(max_iter=1000))
])
pipeline_2 = 파이프라인([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('clf', LogisticRegression(max_iter=1000))
])
pipeline_3 = 파이프라인([
    ('tfidf', TfidfVectorizer(max_features=5000)),
    ('clf', LogisticRegression(max_iter=1000))
])

# 각 모델을 해당 데이터 세트에 대해 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=42)
pipeline_3.fit(X_train_3, y_train_3)
y_pred_3 = pipeline_3.predict(X_val_3)
print(classification_report(y_val_3, y_pred_3))

# 테스트 데이터 세트 로드
test_df = pd.read_csv('test.csv')

# 각각의
```