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Sanskar
Keen Learner and Exp...ย โขย 1m
Day 11 of learning AI/ML as a beginner. Topic: TF-IDF (Term Frequency - Inverse Document Frequency). Yesterday I have talked about N-grams and how they are useful in Bag of Words (BOW) however it has some serious drawbacks and for that reason I am going to talk about TF-IDF. TF-IDF is a tool used to convert text into vectors. I determines how important a word is in a document i.e. it is capable of capturing word importance. Term Frequency as the name suggest means how many times a word is present in a document(sentence). It is calculated by: No. of repetition of words in sentence/No. of words in sentence. Then there is Inverse Document Frequency which assigns less weight to the terms which are used many times across many documents and more weightage to the one which is less used across documents. TF-IDF has some of the major benefits and advantages as compared to its previous tools like BOW, One Hot Encoding etc. Its advantages includes it is intuitive to use, it has fixed vocab size and most importantly it is capable of capturing word importance. Its disadvantages includes the usual Sparsity and the problem of out of vocabulary (OOV). Here are my notes.
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