<div dir="ltr">A note please (to Sebastian Raschka, mrschots). <br><br class="gmail-Apple-interchange-newline"><br><table cellpadding="0" role="presentation" class="gmail-cf gmail-gJ" style="border-collapse:collapse;margin-top:0px;width:auto;font-family:Roboto,RobotoDraft,Helvetica,Arial,sans-serif;font-size:0.875rem;letter-spacing:0.2px;display:block"></table> The OLS model that I used ( where the test score gave me a negative value) was not a good fit.<b> </b> Initial findings showed that t<b>he regression coefficients and the model as a whole were significant, </b>yet ,
finally , it failed in two econometrics tests such as VIF (used for detecting multicollinearity ) and Durbin-Watson test ( used for detecting auto-correlation). <b>Presence of multicollinearity and autocorrelation problems </b> in the model make it unsuitable for prediction. <div>Regards, <div><br></div><div>Samir K Mahajan. </div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Fri, Aug 13, 2021 at 1:41 AM Samir K Mahajan <<a href="mailto:samirkmahajan1972@gmail.com">samirkmahajan1972@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">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. <div><div><br></div><div>My Regards, </div><div><br></div><div>Samir K Mahajan </div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Fri, Aug 13, 2021 at 1:21 AM Sebastian Raschka <<a href="mailto:mail@sebastianraschka.com" target="_blank">mail@sebastianraschka.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
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<div dir="auto">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.<br>
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Cheers,<br>
Sebastian<br>
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<div name="messageReplySection">On Aug 12, 2021, 2:20 PM -0500, Samir K Mahajan <<a href="mailto:samirkmahajan1972@gmail.com" target="_blank">samirkmahajan1972@gmail.com</a>>, wrote:<br>
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Dear <span style="color:rgb(32,33,36);font-size:0.875rem;letter-spacing:0.2px;font-family:Roboto,RobotoDraft,Helvetica,Arial,sans-serif;white-space:nowrap">Christophe Pallier, </span> Reshama Saikh and Tromek Drabas,
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<div><font color="#202124" face="Roboto, RobotoDraft, Helvetica, Arial, sans-serif"><span style="font-size:14px;letter-spacing:0.2px;white-space:nowrap">Thank you for your kind response. </span></font> 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. <b>This is the constraint.</b> 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 (<span style="color:rgb(0,0,0);font-size:14px;white-space:pre-wrap">-5.763335245921777). This negative value breaks the <b>constraint.</b> I just want to highlight that. I think it needs to be corrected.</span> <span style="color:rgb(0,0,0);font-size:14px;white-space:pre-wrap">Rest is up to you .</span></div>
<div><span style="color:rgb(0,0,0);font-size:14px;white-space:pre-wrap"><br></span></div>
<div>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. </div>
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<div>My regards to all of you.</div>
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<div>Samir K Mahajan <br>
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<div dir="ltr" class="gmail_attr">On Thu, Aug 12, 2021 at 12:02 PM Christophe Pallier <<a href="mailto:christophe@pallier.org" target="_blank">christophe@pallier.org</a>> wrote:<br></div>
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<div dir="auto">Simple: despite its name R2 is not a square. Look up its definition.</div>
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<div dir="ltr" class="gmail_attr">On Wed, 11 Aug 2021, 21:17 Samir K Mahajan, <<a href="mailto:samirkmahajan1972@gmail.com" target="_blank">samirkmahajan1972@gmail.com</a>> wrote:<br></div>
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<div>Dear All,</div>
<div>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) </div>
<div>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. </div>
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<div>Regards,</div>
<div>Samir K Mahajan. </div>
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