Quick questions from data science practitioners around the world.
I am attempting to develop a classification model using an unbalanced dataset of messages. Could I take the keyword set from the minority labeled records and boost the TF-IDF values of those particular keywords. I’m looking for a way to improve recall and precision metrics for the minority labeled records. Instead of trying to manipulate …
I’m trying to classify source code into different categories. But the problem with TF-IDF and BOW approach is that they remove arithmetic operators like ++,–,== from the data which play a crucial role in my classification. Is there any way that we could include those? Yes, of course, you can retain those. Most machine learning …
How do I retain stop words/characters when doing machine learning? Read More »
Your article shows that term frequency is counted as: TF(t) = (Number of times term t appears in a document) / (Total number of terms in the document). But other articles on the Web simply use TF as the number of terms occurring in the document, without the denominator. Why do you need to do …
Why do you need to divide term frequency with a number? Read More »