The TF-IDF of a term is calculated by multiplying TF and IDF scores. It is basically, importance of a term is high when it occurs a lot in a given document and rarely in others. In short, commonality within a document measured by TF is balanced by rarity between documents measured by IDF.
Term frequency Inverse document frequency (TFIDF) is a statistical formula to convert text documents into vectors based on the relevancy of the word. It is based on the bag of the words model to create a matrix containing the information about less relevant and most relevant words in the document
Term Frequency (TF)
It is the ratio of the occurrence of the word (w) in document (d) per the total number of words in the documents. With this simple formulation, we are measuring the frequency of a word in the document.
For example, if the sentence has 6 words and contains two “the”, the TF ratio of this word would be (2/6).
Inverse Document Frequency (IDF)
IDF calculates the importance of a word in a corpus D. The most frequently used words like “of, we, are” have little to no significance. It is calculated by dividing the total number of documents in the corpus by the number of documents containing the word.
References:
https://www.kdnuggets.com/2022/09/convert-text-documents-tfidf-matrix-tfidfvectorizer.html
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