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

Samir K Mahajan samirkmahajan1972 at gmail.com
Thu Aug 12 16:32:03 EDT 2021


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 <samirkmahajan1972 at 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 <
> 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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