Indeed , this is basically what I told you (you do not be need to copy textbook stuff: I taught probas/stats) : these are mostly problems for *inference*._______________________________________________On Fri, 13 Aug 2021, 12:03 Samir K Mahajan, <samirkmahajan1972@gmail.com> wrote:
Dear Christophe Pallier,
When we are doing prediction, we are relying on the values of the coefficients of the model created. We are feeding test data on the model for prediction. We may be nterested to see if the OLS estimators(coefficients) are BLUE or not. In the presence of autocorrelation (normally noticed in time series data), residuals are not independent, and as such the OLS estimators are not BLUE in the sense that they don't have minimum variance, and thus no more efficient estimators. Statistical tests (t, F and χ2) may not be valid. We may reject the model to make predictions in such a situation. . We have to rely upon other improved models. There may be issues relating to multicollinearity (in case of multivariable regression model) and heteroscedasticity (mostly seen in cross-section data) too in a model. Can we discard these tools while predicting a model?
Regards,
Samir K Mahajan
_______________________________________________On Fri, Aug 13, 2021 at 1:07 PM Christophe Pallier <christophe@pallier.org> wrote:Actually, multicollinearity and autocorrelation are problems for *inference* more than for *prediction*. For example, if there is autocorrelation, the residuals are not independent, and the degrees of freedom are wrong for the tests in an OLS model (but you can use, e.g., an AR1 model)._______________________________________________On Thu, 12 Aug 2021, 22:32 Samir K Mahajan, <samirkmahajan1972@gmail.com> wrote: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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