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....@python.org> on behalf of Samir K Mahajan <samirkmahajan1...@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.
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