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[ENH] Add macro-averaged mean squared error #846

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#1134
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[ENH] Add macro-averaged mean squared error#846
#1134
@warnbergg

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@warnbergg
warnbergg
opened on Jul 6, 2021 · edited by warnbergg

Describe the solution you'd like

In #780 the macro-averaged mean absolute error was proposed as a metric to the library. Using the same rationale as for that feature, I suggest that also the macro-averaged mean squared error (MAMSE) is added to the library. That way we penalize errors that are further from the ground truth more harshly.

Is this feature something that could be of interest to the greater public?

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hayesall

hayesall commented on Jul 9, 2021

@hayesall
hayesall
on Jul 9, 2021
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Maybe add a squared=True parameter?

That should keep things fairly close with how it's handled elsewhere, e.g. sklearn.metrics.mean_squared_error

warnbergg

warnbergg commented on Jul 10, 2021

@warnbergg
warnbergg
on Jul 10, 2021
Author

@hayesall Yes, that is maybe a better option. I'll create a PR for that!

glemaitre
mentioned this on Jul 10, 2023
glemaitre
closed this as completedin #1023on Jul 10, 2023
glemaitre
reopened this on Jul 10, 2023
Ab2nour
linked a pull request that will close this issue on Apr 20, 2025
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        [ENH] Add macro-averaged mean squared error · Issue #846 · scikit-learn-contrib/imbalanced-learn