The TfidfVectorizer is a feature extraction tool in the Python library scikit-learn (sklearn) that is used for vectorizing text documents into numerical arrays. It converts a collection of raw documents into a matrix where each row represents a document and each column represents a feature (typically, a word or a n-gram). The values in the resulting matrix correspond to the importance of each feature in the respective document, calculated using the term frequency-inverse document frequency (TF-IDF) algorithm. TF-IDF takes into account both the frequency of a term in a document and the frequency of the term across all documents in the collection. This vectorization technique is commonly used in natural language processing (NLP) tasks such as text classification and information retrieval.
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