Dear Christophe Pallier, Reshama Saikh and Tromek Drabas, Thank you for your kind response. Fair enough. I go with you R2 is not a square. However, if you open any book of econometrics, it says R2 is a ratio that lies between 0 and 1. *This is the constraint.* It measures the proportion or percentage of the total variation in response variable (Y) explained by the regressors (Xs) in the model . Remaining proportion of variation in Y, if any, is explained by the residual term(u) Now, sklearn.matrics. metrics.r2_score gives me a negative value lying on a linear scale (-5.763335245921777). This negative value breaks the *constraint. *I just want to highlight that. I think it needs to be corrected. Rest is up to you .
I find that Reshama Saikh is hurt by my email. I am really sorry for that. Please note I never undermine your capabilities and initiatives. You are great people doing great jobs. I realise that I should have been more sensible. My regards to all of you. Samir K Mahajan On Thu, Aug 12, 2021 at 12:02 PM Christophe Pallier <christo...@pallier.org> wrote: > Simple: despite its name R2 is not a square. Look up its definition. > > On Wed, 11 Aug 2021, 21:17 Samir K Mahajan, <samirkmahajan1...@gmail.com> > wrote: > >> Dear All, >> I am amazed to find negative values of sklearn.metrics.r2_score and >> sklearn.metrics.explained_variance_score in a model ( cross validation of >> OLS regression model) >> However, what amuses me more is seeing you justifying negative >> 'sklearn.metrics.r2_score ' in your documentation. This does not >> make sense to me . Please justify to me how squared values are negative. >> >> Regards, >> Samir K Mahajan. >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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