From the course: NLP with Python for Machine Learning Essential Training
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Inverse document frequency weighting - Python Tutorial
From the course: NLP with Python for Machine Learning Essential Training
Inverse document frequency weighting
- [Instructor] So we've gone over the count vectorizor and n-grams. Now we're going to touch on the last vectorization method that we'll be covering, Term Frequency-Inverse Document Frequency, often referred to as TF-IDF. These aren't the only three methods of vectorizing, but they're the only three that we're going to cover here because they are the most popular. So what is TF-IDF? TF-IDF creates a document term matrix, where there's still one row per text message and the columns still represent single unique terms. But instead of the cells representing the count, the cells represent a weighting that's meant to identify how important a word is to an individual text message. This formula lays out how this weighting is determined. It may look a little bit intimidating, but it's actually quite simple. You start with this TF term, which is just the number of times that term I occurs in text message J, divided by the number of terms in text message J. It's just the percent of terms in…
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