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 (My next step is to open an issue and ask to deprecate completely SAMME.R because it is actually an unpublished algorithm that triggers some divergent behaviour). |
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Co-authored-by: Jérémie du Boisberranger <[email protected]>
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@ogrisel do you agree with the last changes ? |
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LGTM. However I made a mistaken when resolving the changelog conflict. Let me push a fix before merging.
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…kit-learn#26521) Co-authored-by: Jérémie du Boisberranger <[email protected]> Co-authored-by: Olivier Grisel <[email protected]>
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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.