The sklearn.feature_extraction.text.TfidfVectorizer is a Python module that enables the conversion of text data into numerical representations in the form of TF-IDF (Term Frequency-Inverse Document Frequency) vectors. TF-IDF is a widely used statistical measure in natural language processing and information retrieval, used to evaluate the importance of a word in a document with respect to a collection of documents. This vectorizer computes the TF-IDF values for each word in the input text data, creating a vector for each document that represents the word frequencies and their relevance within the document corpus. This numerical representation can then be used as input for various machine learning algorithms in tasks such as text classification, clustering, and information retrieval.
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