A note please (to Sebastian Raschka, mrschots).
The OLS model that I used ( where the test score gave me a negative value) was not a good fit. Initial findings showed that t*he regression coefficients and the model as a whole were significant, *yet , finally , it failed in two econometrics tests such as VIF (used for detecting multicollinearity ) and Durbin-Watson test ( used for detecting auto-correlation). *Presence of multicollinearity and autocorrelation problems * in the model make it unsuitable for prediction. Regards, Samir K Mahajan. On Fri, Aug 13, 2021 at 1:41 AM Samir K Mahajan <samirkmahajan1...@gmail.com> wrote: > 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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