What is TF/IDF vectorization?

By Dinesh, 5 months ago
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Where to use TD/IDF vectorization?

Machine learning
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TF–IDF is short for term frequency-inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.

It is often used as a weighting factor in information retrieval and text mining.

The TF–IDF value increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus, which helps to adjust for the fact that some words appear more frequently in general.

TF-IDF = Term Frequency (TF) * Inverse Document Frequency (IDF)

Terminology :

  • t — term (word)
  • d — document (set of words)
  • N — count of corpus
  • corpus — the total document set

TF is individual to each document and word, hence we can formulate TF as follows.

tf(t,d) = count of t in d / number of words in d

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