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[MRG] FIX ignore null weight when computing estimator error in AdaBoostRegressor #14294

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Merged
merged 30 commits into from
Oct 25, 2019
Merged

[MRG] FIX ignore null weight when computing estimator error in AdaBoostRegressor #14294

merged 30 commits into from
Oct 25, 2019

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glemaitre
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@glemaitre glemaitre commented Jul 8, 2019 •
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AdaBoostRegressor suffers from a bug where the error was normalized using the max of the absolute error on all prediction even if they were corresponding to a null-weight in sample_weigth

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rth commented Jul 8, 2019

BTW, this seems related to #14286 which investigates a similar issue with SVM.

@glemaitre glemaitre mentioned this pull request Jul 8, 2019
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@glemaitre glemaitre changed the title FIX ignore null weight when computing estimator error in AdaBoostRegressor [MRG] FIX ignore null weight when computing estimator error in AdaBoostRegressor Jul 8, 2019
@rth rth mentioned this pull request Jul 12, 2019
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@glemaitre glemaitre force-pushed the is/fix_adaboost_regressor branch from ede568e to 6902eff Compare September 10, 2019 07:45
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ping @rth @jeremiedbb I think that you are the best to review this bug fix.

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jeremiedbb reviewed Sep 10, 2019
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Not using points with zero weight to compute the error seems fair.

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@glemaitre glemaitre force-pushed the is/fix_adaboost_regressor branch from 2f27d59 to 810c637 Compare September 12, 2019 09:53
jeremiedbb
jeremiedbb approved these changes Sep 12, 2019
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besides minor comments, LGTM.

glemaitre and others added 4 commits September 12, 2019 17:36
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@adrinjalali I'm pinging you aggressively ;)

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adrinjalali reviewed Sep 27, 2019
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I need to double check a few things before I can be sure about its correctness and completeness. But I was wondering if it'd be easier to mask the samples and the sample weight right at the beginning of the fit, and continue with all of what's left.

P.S. thanks for the very aggressive ping :P

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I was wondering if it'd be easier to mask the samples and the sample weight right at the beginning of the fit, and continue with all of what's left.

Nop because the bootstrapping will lead to different results since the input data will be different due to masking.

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@adrinjalali Any other comments?

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adrinjalali approved these changes Oct 23, 2019
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Other than the small nit, LGTM, thanks @glemaitre

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Ready to be merged @adrinjalali @jeremiedbb

@adrinjalali adrinjalali merged commit 1888a96 into scikit-learn:master Oct 25, 2019
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Thanks @glemaitre :)

@ryuwd ryuwd mentioned this pull request Nov 27, 2020
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@adrinjalali adrinjalali adrinjalali approved these changes

@jeremiedbb jeremiedbb jeremiedbb approved these changes

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