使用tf/idf获取高频特征词并输出，以此训练分类模型并进行模型评估，代码

首先，我们需要导入所需的库：

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

接下来，我们读取数据并分割为训练集和测试集：

data = pd.read_csv('data.csv')
X_train, X_test, y_train, y_test = train_test_split(data['text'], data['label'], test_size=0.2, random_state=42)

然后，我们使用 TfidfVectorizer 对文本进行特征提取：

tfidf = TfidfVectorizer(stop_words='english')
X_train_tfidf = tfidf.fit_transform(X_train)
X_test_tfidf = tfidf.transform(X_test)

接下来，我们使用 MultinomialNB 训练分类模型：

clf = MultinomialNB()
clf.fit(X_train_tfidf, y_train)

最后，我们评估模型并输出结果：

y_pred = clf.predict(X_test_tfidf)
print('Accuracy:', accuracy_score(y_test, y_pred))
print('Confusion Matrix:\n', confusion_matrix(y_test, y_pred))

完整代码如下：

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

data = pd.read_csv('data.csv')
X_train, X_test, y_train, y_test = train_test_split(data['text'], data['label'], test_size=0.2, random_state=42)

tfidf = TfidfVectorizer(stop_words='english')
X_train_tfidf = tfidf.fit_transform(X_train)
X_test_tfidf = tfidf.transform(X_test)

clf = MultinomialNB()
clf.fit(X_train_tfidf, y_train)

y_pred = clf.predict(X_test_tfidf)
print('Accuracy:', accuracy_score(y_test, y_pred))
print('Confusion Matrix:\n', confusion_matrix(y_test, y_pred))