基于TfidfVectorizer()写一个朴素贝叶斯代码 我的文件时excel

首先，需要将Excel文件读入Python中。可以使用pandas库来读取Excel文件：

import pandas as pd

df = pd.read_excel('your_file.xlsx')

然后，需要对文本进行分词和向量化。可以使用sklearn库中的TfidfVectorizer()来完成这一步：

from sklearn.feature_extraction.text import TfidfVectorizer

tfidf = TfidfVectorizer()
X = tfidf.fit_transform(df['text_column'])
y = df['label_column']

这里假设Excel文件中有一个名为`text_column`的列存储文本数据，另一个名为`label_column`的列存储标签数据。

接下来，使用朴素贝叶斯算法进行训练和预测：

from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

nb = MultinomialNB()
nb.fit(X_train, y_train)

y_pred = nb.predict(X_test)

最后，可以使用sklearn库中的各种评估方法来评估模型的性能，例如准确率、精确率、召回率和F1值等。

完整代码如下：

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# 读取Excel文件
df = pd.read_excel('your_file.xlsx')

# 分词和向量化
tfidf = TfidfVectorizer()
X = tfidf.fit_transform(df['text_column'])
y = df['label_column']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练朴素贝叶斯模型
nb = MultinomialNB()
nb.fit(X_train, y_train)

# 预测测试集
y_pred = nb.predict(X_test)

# 评估模型性能
print('Accuracy:', accuracy_score(y_test, y_pred))
print('Precision:', precision_score(y_test, y_pred, average='macro'))
print('Recall:', recall_score(y_test, y_pred, average='macro'))
print('F1 score:', f1_score(y_test, y_pred, average='macro'))