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Can you explain how tf-idf works in detail?

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Answers

Anonymous

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TF-IDF is an approach based off ngrams. TF or term frequency is the count or ratio of words in a text blob. IDF or inverse document frequency finds the importance of the word across multiple text blobs. Together, they will be used to find important words in a text and their intersity depending on how many times it has been used. TF-IDF is used in multiple applications like text classification etc. The downside of the approach is that the vector that it creates is very sparse and as it's an ngram based approach it does not utilize context information.
  • Can you explain how tf-idf works in detail?
  • What is your understanding of the term tf-idf? How does it help in information retrievals?
  • Could you elaborate on the significance of tf-idf in natural language processing?
  • How does the tf-idf algorithm rank words based on their importance in a document and what is the range of values for tf-idf scores?
  • As an expert in information retrieval, could you share how tf-idf can be used to identify relevant content among the millions of data available on the internet?
  • What is the impact of stop-word removal on tf-idf analysis?
  • Why tf-idf is considered better than other techniques to determine the relevance of a document based on search queries?
  • How can tf-idf help increase the accuracy of text classification and clustering algorithms?
  • In your opinion, which applications of tf-idf are the most successful in text mining, and why?
  • What is tf-idf ?
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Interview question asked to Machine Learning Engineers interviewing at Pluralsight, SAP, TripAdvisor and others: Can you explain how tf-idf works in detail?.