Thanks to all of you for your kind response. Indeed, it is a great learning experience. Yes, econometrics books too create models for prediction, and programming really makes things better in a complex world. My understanding is that machine learning does depend on econometrics too.
My Regards, Samir K Mahajan On Fri, Aug 13, 2021 at 1:21 AM Sebastian Raschka <m...@sebastianraschka.com> wrote: > The R2 function in scikit-learn works fine. A negative means that the > regression model fits the data worse than a horizontal line representing > the sample mean. E.g. you usually get that if you are overfitting the > training set a lot and then apply that model to the test set. The > econometrics book probably didn't cover applying a model to an independent > data or test set, hence the [0, 1] suggestion. > > Cheers, > Sebastian > > > On Aug 12, 2021, 2:20 PM -0500, Samir K Mahajan < > samirkmahajan1...@gmail.com>, wrote: > > > 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 >> > _______________________________________________ > 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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