Sure, here's an outline of the code you can use to train separate machine learning models for each group:
```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')

# Map the labels to new groups
label_map = {0: 0, 1: 1, 2: 0, 3: 1, 4: 1, 5: 2, 6: 2, 7: 3}
train_df['group'] = train_df['label'].map(label_map)

# Define four datasets based on the new label groups
group_0_df = train_df[train_df['group'] == 0]
group_1_df = train_df[train_df['group'] == 1]
group_2_df = train_df[train_df['group'] == 2]
group_3_df = train_df[train_df['group'] == 3]

# 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['group'], 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['group'], 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['group'], 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['group'], 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
```
각 그룹에 대해 별도의 머신 러닝 모델을 학습하는 데 사용할 수 있는 코드의 개요는 다음과 같습니다:
'''python
import pandas as pd
sklearn.model_selection에서 train_test_split을 가져옵니다.
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')

# 라벨을 새 그룹에 매핑
label_map = {0: 0, 1: 1, 2: 0, 3: 1, 4: 1, 5: 2, 6: 2, 7: 3}
train_df['group'] = train_df['label'].map(label_map)

# 새 레이블 그룹을 기반으로 4개의 데이터 세트를 정의합니다.
group_0_df = train_df[train_df['group'] == 0]
group_1_df = train_df[train_df['group'] == 1]
group_2_df = train_df[train_df['group'] == 2]
group_3_df = train_df[train_df['group'] == 3]

# 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['group'], 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['group'], 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['group'], 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['group'], 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'
```