TF-IDF, or Term Frequency-Inverse Document Frequency, is a statistical measure used in natural language processing and information retrieval to assess the importance of a term within a document relative to a collection of documents. It has two components:

Term Frequency (TF): This measures the frequency of a term within a document, indicating how often the term occurs relative to the total number of words in the document.
Inverse Document Frequency (IDF): This measures the rarity of a term across a collection of documents, highlighting terms that are unique or rare across the corpus.

Together, TF-IDF calculates a weight for each term that reflects its importance in the context of a specific document and the entire document collection. It helps identify key terms that are both common within a document and distinctive across the corpus, aiding tasks such as document classification, information retrieval, and text analysis.

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