FIX make sure the decision function of weak learner is symmetric #26521
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Since this is a bug that I found while looking at #26486, maybe @adrinjalali and @StefanieSenger are interested to look at it. (My next step is to open an issue and ask to deprecate completely SAMME.R because it is actually an unpublished algorithm that triggers some divergent behaviour). |
jeremiedbb
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ogrisel
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Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
jeremiedbb
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@ogrisel do you agree with the last changes ? |
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Solved the conflicts |
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…kit-learn#26521) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
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…kit-learn#26521) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
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closes #26520
From the original paper, when considering the multiclass exponential-loss, it is expected that the decision function of each weak-learner to be symmetric around zero.
It concretely means that the predicted class should be
(n_classes - 1) / n_classes * weightand the other classes-1 / n_classes * weight.This PR correct the
decision_functionand thestaged_decision_functionand add a non-regression case where we use a single learner to make sure that we respect this constraint.