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ENH Consistent loss name for absolute error #19733
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rth
merged 13 commits into
scikit-learn:main
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lorentzenchr:consistent_absolute_error
May 10, 2021
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b22aa54
MNT deprecate mae criterion in tree module
lorentzenchr 0bdeb7b
MNT deprecate loss absolute_loss in RANSACRegressor
lorentzenchr 4fe9982
MNT deprecate loss least_absolute_deviation in HGBRegressor
lorentzenchr b9f5677
MNT deprecate criterion mae in forests
lorentzenchr 9c525e0
MNT deprecate loss lad in GradientBoostingRegressor
lorentzenchr a6b2796
DOC replace least_absolute_deviation in user guide
lorentzenchr 3254391
DOC add whatsnew entry
lorentzenchr 2e65634
TST nicer deprecation tests
lorentzenchr f6f1816
MNT deal with mae in _make_estimator
lorentzenchr 9a8d1c7
TST replace remaining instances of "mae" in test_forest.py
lorentzenchr 125162d
TST nicer depcrecation test for HGBT
lorentzenchr 707453e
Merge branch 'main' into consistent_absolute_error
lorentzenchr 1aeed2b
Merge branch 'main' into consistent_absolute_error
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For deprecations, we normally keep the original code and then configure the test to ignore the
FutureWarnings
. This is to make sure that we still support the original behavior during the deprecation period.For this specific case, there is an argument for just replacing the name, since it is a simple name change and adding another loss that does exactly the same thing would increase the runtime of tests.
I am +0.25 with replacing the name directly in the tests as you already done in this PR.
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Good to know for future deprecations, although it's not my intend to become a deprecation master:smirk:
Equivalence of new and old models'
predict
is always tested intest_loss_deprecated
. So how is your rounding policy (and where is the 0.75 going to)?Thanks for your support on this little project!
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It's more of an "absence of 0.75". Thinking about this again, I am now at +0.75 🤣.