FIX/MNT do not support OOB score for multiclass-multioutput and additional refactoring #19162
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@ogrisel it should be OK now to have a first review. |
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@thomasjpfan @ogrisel @NicolasHug @adrinjalali @amueller Do you want to have a look at this PR. This could be useful to further review the permutation importance using the OOB. |
Co-authored-by: Adrin Jalali <[email protected]>
ogrisel
approved these changes
Jan 21, 2021
Co-authored-by: Olivier Grisel <[email protected]>
ogrisel
reviewed
Jan 21, 2021
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ping @adrinjalali for a second approval :) |
adrinjalali
approved these changes
Jan 25, 2021
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Thanks @glemaitre . LGTM, if you're not worried about the comment bellow, feel free to merge :)
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| else: | ||
| # for regression, n_classes_ does not exist and we create an empty | ||
| # axis to be consistent with the classification case and make | ||
| # the array operations compatible with the 2 settings | ||
| oob_pred_shape = (n_samples, 1, n_outputs) |
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hopefully there is no classifier out there which has an effective n_classes_ > 1 and yet not setting the right flag to say it's a classifier :D
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It should not pass our common test then I think.
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Refactor the OOB scoring in forest.
TODO:
sklearndoes not have any metric for this case.