[MRG] Fixed gradient computation in gradient boosting for multiclass classification #12715
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Indeed this PR seems to fix a bug, however it's probably not easy to write a non-regression test as the effect is probably very subtle. |
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It's hard to tell how strong the impact of the changes would be... My gut feeling is that it would be pretty minimal. |
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I think nobody will notice the bug in practice. Not worth the backport a priori. But @jnothman feel free to do the backport if you disagree. |
ogrisel
approved these changes
Dec 14, 2018
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Thanks @NicolasHug |
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…assification (scikit-learn#12715) * Fixed gradient update * Added whatsnew entry
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…assification (scikit-learn#12715) * Fixed gradient update * Added whatsnew entry
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…class classification (scikit-learn#12715)" This reverts commit df30aa9.
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…class classification (scikit-learn#12715)" This reverts commit df30aa9.
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…assification (scikit-learn#12715) * Fixed gradient update * Added whatsnew entry
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Reference Issues/PRs
Fixes #12696
What does this implement/fix? Explain your changes.
The gradients should be evaluated at iteration
i - 1, that is, using the values ofy_predat iterationi - 1In the current implementation,
y_predis partially updated during the loop over theKtrees of the current iteration. This means that the gradients aren't evaluated at iterationi - 1anymore. It's a mix between iterationi - 1and iterationi.The current fix copies
y_predbefore looping over the trees to avoid this.I'm not sure how to propose a non-regression test for this.
Any other comments?