[MRG] FIX use a stump as base estimator in RUSBoostClassifier

[chkoar]

What does this implement/fix? Explain your changes.

I don't think that the referenced paper mentions something about stumps but this change keep us in line with the original implementation of AdaBoostClassifier

[glemaitre]

I think that you are right. We should actually call the parent class then:
https://github.com/scikit-learn/scikit-learn/blob/7389dba/sklearn/ensemble/weight_boosting.py#L414

[pep8speaks]

Hello @chkoar! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2019-06-11 21:51:45 UTC

[chkoar]

@glemaitre do you have any clue why this test is failing? Isn't it weird?

[codecov]

Codecov Report

Merging #545 into master will decrease coverage by 0.05%.
The diff coverage is 100%.

@@ Coverage Diff @@ ## master #545 +/- ## ========================================== - Coverage 98.88% 98.83% -0.06%  ========================================== Files 84 82 -2 Lines 5184 5043 -141 ========================================== - Hits 5126 4984 -142  - Misses 58 59 +1

Impacted Files	Coverage Δ
imblearn/ensemble/tests/test_weight_boosting.py	100% <100%> (ø)	⬆️
imblearn/ensemble/_weight_boosting.py	97.72% <100%> (+0.91%)	⬆️
imblearn/over_sampling/_smote.py	95.54% <0%> (-1.72%)	⬇️
imblearn/utils/estimator_checks.py	96.74% <0%> (-0.16%)	⬇️
imblearn/tests/test_pipeline.py	99% <0%> (-0.07%)	⬇️
imblearn/tests/test_common.py	97.43% <0%> (-0.07%)	⬇️
...n/under_sampling/_prototype_selection/_nearmiss.py	98.73% <0%> (-0.04%)	⬇️
...ling/_prototype_selection/_random_under_sampler.py	100% <0%> (ø)	⬆️
imblearn/utils/tests/test_docstring.py	76.92% <0%> (ø)	⬆️
...mpling/_prototype_generation/_cluster_centroids.py	100% <0%> (ø)	⬆️
... and 19 more

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[chkoar]

@glemaitre early stopping issue

[chkoar]

@glemaitre as you can see using a stump as the default estimator I believe that the performance ain't so good. So, what do you propose? To make a benchmark using stump and a full decision tree and decide then, make the stump default to be in line with scikit-learn or leave the full grown tree as default?