[scikit-learn] Regarding negative value of sklearn.metrics.r2_score and sklearn.metrics.explained_variance_score

[Samir K Mahajan]

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 at 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 at 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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