Regarding negative value of sklearn.metrics.r2_score and sklearn.metrics.explained_variance_score
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.
Hi Samir, In the documentation there’s a link to how the coefficient of determination is defined: https://en.m.wikipedia.org/wiki/Coefficient_of_determination From this it is easy to see when the values can become negative: when the model performs significantly worse than the baseline (predicting average for each observation). Common misconception is that the ‘squaredness’ is of some single value but in here (per CoD’s definition) it’s the ration of the squared distances of the baseline model and the estimated one. Hope this helps, -Tom Sent on the go ________________________________ From: scikit-learn <scikit-learn-bounces+drabas.t=gmail.com@python.org> on behalf of Samir K Mahajan <samirkmahajan1972@gmail.com> Sent: Wednesday, August 11, 2021 12:16:34 PM To: scikit-learn@python.org <scikit-learn@python.org> Subject: [scikit-learn] Regarding negative value of sklearn.metrics.r2_score and sklearn.metrics.explained_variance_score 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.
Hello Samir, The tone of your email is disrespectful. For any project, but particularly so for an open source project. It is not for this community. Please review the Code of Conduct for this library. http://scikit-learn.org/stable/developers/contributing.html Regards, Reshama
On Aug 11, 2021, at 3:18 PM, 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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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.
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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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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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There is no constraint, that’s the point since nothing limits you to have a model with crap predictions leading to be worse than to just predict the target’s mean for every data point. If you do so —> negative R2. Best Regards, Em qui., 12 de ago. de 2021 às 16:21, Samir K Mahajan < samirkmahajan1972@gmail.com> escreveu:
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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-- Schots
In the simplest case of a simple linear regression what you wrote holds true: the explained variance is simply a sum of variance explained by the model and the residual variability that cannot be explained, and that would always lie between 0 and 1. e.g. here: https://online.stat.psu.edu/stat500/lesson/9/9.3 However, this would be quite hard to do for more complex models (even for a multivariate linear regression) thus a need for a more general definition like here: https://en.wikipedia.org/wiki/Coefficient_of_determination or here https://www.investopedia.com/terms/r/r-squared.asp. I can easily envision a situation where data has outliers (i.e. data is not clean enough to be used in modeling) that it'd render a model that performs worse than a base model of simply taking average as a prediction for each observation. Cheers, -Tom On Thu, Aug 12, 2021 at 12:19 PM 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.
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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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@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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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@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@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.
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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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 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@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@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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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 *χ*2) 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 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@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@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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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 *χ*2) 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 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@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@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. > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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Hey Samir, this blog post has some more details on the difference between the square of the correlation coefficient and the coefficient of determination: danshiebler.com/2017-06-25-metrics/ On Fri, Aug 13, 2021 at 6:10 AM Christophe Pallier <christophe@pallier.org> wrote:
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 *χ*2) 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 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@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@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. >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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-- danshiebler.com (973) - 518 - 0886
Dear Chrisophe, I think you are oversimplifying by saying econometrics tools are for inference. Forecasting and prediction are integral parts of econometric analysis. Econometricians forecast by inferring the right conclusion about the model . I wish to convey to you that I teach both statistics and econometrics, and am now learning ML. There is a fundamental difference among statistics, econometrics and machine learning. Regards, Samir K Mahajan On Fri, Aug 13, 2021 at 3:39 PM Christophe Pallier <christophe@pallier.org> wrote:
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 *χ*2) 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 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@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@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. >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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Hi Samir, the following visualization might be useful for gaining intuition on the meaning of a negative r2: https://gist.github.com/WittmannF/02060b45ce3ec9239898a5b91df2564e A negative r2 is reflects into a model predicting the opposite trend of the data. On Sat, Aug 14, 2021, 03:17 Samir K Mahajan <samirkmahajan1972@gmail.com> wrote:
Dear Chrisophe, I think you are oversimplifying by saying econometrics tools are for inference. Forecasting and prediction are integral parts of econometric analysis. Econometricians forecast by inferring the right conclusion about the model . I wish to convey to you that I teach both statistics and econometrics, and am now learning ML. There is a fundamental difference among statistics, econometrics and machine learning. Regards,
Samir K Mahajan
On Fri, Aug 13, 2021 at 3:39 PM Christophe Pallier <christophe@pallier.org> wrote:
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 *χ*2) 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 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@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@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. >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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participants (8)
-
Christophe Pallier -
Dan Shiebler -
Fernando Marcos Wittmann -
mrschots -
Reshama Shaikh -
Samir K Mahajan -
Sebastian Raschka -
Tomek Drabas