How deeply does Machine Learning Algorithms in Depth cover term frequency-inverse document frequency?

Defines the weighting formula and its variables, but does not derive or implement the method step by step.

Term frequency-inverse document frequency

As explained in Machine Learning Algorithms in Depth

TF-IDF is a term-weighting technique that combines a term's frequency in a document with the inverse frequency of documents containing that term.

Term frequency–inverse document frequency (TF-IDF) weights terms by combining their frequency within a document with the inverse frequency of documents containing them. It adjusts the term-document matrix so words common across a corpus contribute less and discriminative words contribute more. section 9.1

For a corpus of D documents, let nt denote the number of documents containing term t. The inverse-document-frequency factor is log(D/nt), which approaches zero as a term appears in more documents; applying it to word counts produces smoothed weights for downstream topic modeling. section 9.1 section 9.8

TF-IDF fits into document representation as follows:

Represent documents with a bag-of-words term-document matrix whose columns or rows encode vocabulary-term counts. section 9.1
Apply inverse-document-frequency weighting to emphasize terms that distinguish documents. section 9.1
Extend the representation from unigrams to n-grams when larger structural units are useful. section 9.1

How Machine Learning Algorithms in Depth covers it

Cover of Machine Learning Algorithms in Depth

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Also explained in

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Natural Language Processing in Action, Second EditionA TF-IDF model represents text using term-frequency and inverse-document-frequency weights for terms in documents.
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AI Agents in Action, Second EditionTF-IDF is a term-weighting technique that scores a word by its frequency in a document relative to its frequency across a collection of documents.
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Elasticsearch in Action, Second EditionA term frequency–inverse document frequency algorithm ranks documents using query-term frequency in documents and term rarity across the collection.
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AI-Powered SearchTF-IDF is a text-weighting metric that combines term frequency and inverse document frequency to estimate a term's importance within a document collection.
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Math and Architectures of Deep LearningA document-representation weighting scheme that combines term frequency with inverse document frequency to emphasize discriminative words.
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Data Without LabelsTerm frequency-inverse document frequency (TF-IDF) assigns a term a weight based on its frequency in a document and its inverse frequency across a document collection.
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AI Agents in ActionTF-IDF is a term-weighting method that scores words by their frequency in a document relative to their frequency across a document collection.
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Machine Learning System DesignA text-weighting representation that scores terms by their frequency in a document relative to their frequency across documents.
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Generative AI in ActionTF-IDF is a statistical weighting scheme that scores a term according to its frequency in a document and its rarity across a document collection.

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Related concepts

inverse document frequencyhas part · Inverse document frequency is a corpus-based measure of term rarity that assigns lower weights to terms appearing in many documents and higher weights to rarer terms.
term frequencyhas part · Term frequency is the raw or normalized count of a term’s occurrences within a document or other text unit.
lexical searchused by · Lexical search retrieves or ranks items by matching query terms or other surface-text features against indexed content, often using sparse representations and statistical weighting.
embeddingcontrasts with · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
BM25alternative to · BM25 is a probabilistic information-retrieval ranking function that scores documents for a query using term frequency, inverse document frequency, and document-length normalization.
Word2Vecalternative to · Word2Vec is a family of shallow neural models that learns vector representations of words from their contexts in text.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Algorithms in Depth. The section links point to the parts of the book it draws on.