使用朴素贝叶斯、LOGIT、XGBOOST算法对文本进行文档分类实现代码

以下是使用朴素贝叶斯、LOGIT、XGBOOST算法对文本进行文档分类的示例代码：

## 1. 数据预处理

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split

# 读取数据
data = pd.read_csv('text_classification.csv')

# 划分训练集和测试集
train, test = train_test_split(data, test_size=0.2, random_state=42)

# 将文本转化为向量
from sklearn.feature_extraction.text import CountVectorizer

vectorizer = CountVectorizer()
train_vec = vectorizer.fit_transform(train['text'])
test_vec = vectorizer.transform(test['text'])

# 将标签转化为数字
from sklearn.preprocessing import LabelEncoder

label_encoder = LabelEncoder()
train_label = label_encoder.fit_transform(train['label'])
test_label = label_encoder.transform(test['label'])

## 2. 朴素贝叶斯算法

from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score

nb = MultinomialNB()
nb.fit(train_vec, train_label)
nb_pred = nb.predict(test_vec)
nb_acc = accuracy_score(test_label, nb_pred)
print('朴素贝叶斯算法的准确率为：', nb_acc)

## 3. LOGIT算法

from sklearn.linear_model import LogisticRegression

logit = LogisticRegression()
logit.fit(train_vec, train_label)
logit_pred = logit.predict(test_vec)
logit_acc = accuracy_score(test_label, logit_pred)
print('LOGIT算法的准确率为：', logit_acc)

## 4. XGBOOST算法

import xgboost as xgb

dtrain = xgb.DMatrix(train_vec, label=train_label)
dtest = xgb.DMatrix(test_vec, label=test_label)

params = {
    'objective': 'multi:softmax',
    'num_class': len(label_encoder.classes_)
}

xgb_model = xgb.train(params, dtrain, num_boost_round=100)
xgb_pred = xgb_model.predict(dtest)
xgb_acc = accuracy_score(test_label, xgb_pred)
print('XGBOOST算法的准确率为：', xgb_acc)

注意：以上代码仅供参考，具体实现需要根据数据集的情况进行调整。