Hi Sole, It’s been a long time, but I remember helping with drafting the Tf-idf text in the documentation as part of a scikit-learn sprint at SciPy a looong time ago where I mentioned this difference (since it initially surprised me, because I couldn’t get it to match my from-scratch implementation). As far as I remember, the sklearn version addressed some instability issues for certain edge cases.
I am not sure if that helps, but I have briefly compared the textbook vs the sklearn tf-idf here: https://github.com/rasbt/machine-learning-book/blob/main/ch08/ch08.ipynb Best, Sebastian -- Sebastian Raschka, PhD Machine learning and AI researcher, https://sebastianraschka.com Staff Research Engineer at Lightning AI, https://lightning.ai On May 28, 2024 at 9:43 AM -0500, Sole Galli via scikit-learn <scikit-learn@python.org>, wrote: > Hi guys, > > I'd like to understand why sklearn's implementation of tf-idf is different > from the standard textbook notation as described in the docs: > https://scikit-learn.org/stable/modules/feature_extraction.html#tfidf-term-weighting > > Do you have any reference that I could take a look at? I didn't manage to > find them in the docs, maybe I missed something? > > Thank you! > > Best wishes > Sole > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn
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