将训练得到的朴素贝叶斯模型MultinomialNB模型classifier保存到本地models目录中在一个另一个py文件中 从目录中读取保存的模型并用data目录下的clean400csv测试模型准确率

保存模型代码：

import joblib
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd

# 读取数据
df = pd.read_csv('data/clean400.csv', encoding='utf-8')

# 特征提取
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(df['content'])
y = df['category']

# 训练模型
classifier = MultinomialNB(alpha=0.01)
classifier.fit(X, y)

# 保存模型
joblib.dump(classifier, 'models/classifier.pkl')

读取模型并测试准确率代码：

import joblib
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd

# 读取数据
df = pd.read_csv('data/clean400.csv', encoding='utf-8')

# 特征提取
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(df['content'])
y = df['category']

# 读取模型
classifier = joblib.load('models/classifier.pkl')

# 测试准确率
accuracy = classifier.score(X, y)
print('准确率:', accuracy)
``