TF-IDF
TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical measure used in natural language processing and information retrieval to evaluate the importance of a word in a document relative to a collection of documents. It combines term frequency (how often a word appears in a document) with inverse document frequency (how rare the word is across the collection), helping to identify key terms that are distinctive to specific documents. This technique is widely applied in text mining, search engines, and document classification tasks.
Developers should learn TF-IDF when working on projects involving text analysis, such as building search engines, recommendation systems, or spam filters, as it provides a simple yet effective way to quantify word relevance. It is particularly useful for tasks like document similarity scoring, keyword extraction, and improving search result rankings by highlighting terms that are significant in a specific context but not common across all documents. For example, in a news article dataset, TF-IDF can help identify unique terms that distinguish sports articles from politics articles.