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 the 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 <samirkmahajan1972@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 MahajanOn Fri, Aug 13, 2021 at 1:21 AM Sebastian Raschka <mail@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 <samirkmahajan1972@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 <christophe@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, <samirkmahajan1972@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.
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