sklearn实现基于TF-IDF的KNN新闻标题文本分类_tfidnwk

要使用scikit-learn实现基于TF-IDF的KNN新闻标题文本分类，可以按照以下步骤进行操作：

 

1.导入所需的库和模块：

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from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.neighbors import KNeighborsClassifier

from sklearn.model_selection import train_test_spli

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2.准备数据集，将新闻标题文本和对应的标签（类别）存储在两个列表中：

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titles = ['news title 1', 'news title 2', ..., 'news title n']
labels = ['category 1', 'category 2', ..., 'category n']
3.对标题文本进行TF-IDF向量化处理：

tfidf_vectorizer = TfidfVectorizer()

tfidf_matrix = tfidf_vectorizer.fit_transform(titles)

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4.将TF-IDF矩阵和标签列表拆分为训练集和测试集：

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X_train, X_test, y_train, y_test = train_test_split(tfidf_matrix, labels, test_size=0.2, random_state=42)

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5.训练KNN分类器：

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knn_classifier = KNeigh