There is no constraint, that’s the point since nothing limits you to have a model with crap predictions leading to be worse than to just predict the target’s mean for every data point. If you do so —> negative R2. Best Regards, Em qui., 12 de ago. de 2021 às 16:21, Samir K Mahajan < samirkmahajan1972@gmail.com> escreveu:
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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