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

[Samir K Mahajan]

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 <mail at 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 at 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 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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