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[WIP] EHN: Implementation of BalancedRandomForestClassifier #459

merged 39 commits into from Sep 6, 2018


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closes #456

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pep8speaks commented Aug 26, 2018

Hello @glemaitre! Thanks for updating the PR.

Comment last updated on September 06, 2018 at 12:29 Hours UTC

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@chkoar I made a quick implementation of a balanced random forest classifier.
I tried to keep the changes minimal. The issue is that most of the code rely that the base estimators are trees, calling some private functions. Therefore, we cannot easily use pipeline as in the bagging case.

If you could have a look at it. It would be nice to have a second opinion.

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Note that this can work only with the release 0.20 which is the reason for the failing.

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chkoar commented Aug 27, 2018

We don't implement this via Bagging in order to get feature importances out of the box, right?

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glemaitre commented Aug 27, 2018 via email

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massich commented Aug 28, 2018

There's some issue with the init. I'll check it out

@glemaitre glemaitre changed the title EHN: Implementation of BalancedRandomForestClassifier [WIP] EHN: Implementation of BalancedRandomForestClassifier Aug 29, 2018
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codecov bot commented Sep 5, 2018

Codecov Report

Merging #459 into master will increase coverage by <.01%.
The diff coverage is 98.96%.

Impacted file tree graph

@@            Coverage Diff            @@
##           master    #459      +/-   ##
+ Coverage   98.69%   98.7%   +<.01%     
  Files          75      77       +2     
  Lines        4538    4720     +182     
+ Hits         4479    4659     +180     
- Misses         59      61       +2
Impacted Files Coverage Δ
imblearn/ensemble/ 100% <ø> (ø) ⬆️
imblearn/ensemble/tests/ 100% <100%> (ø)
imblearn/utils/ 100% <100%> (ø) ⬆️
imblearn/ensemble/ 100% <100%> (ø) ⬆️
...ling/_prototype_selection/ 100% <100%> (ø) ⬆️
imblearn/ensemble/ 98.13% <98.13%> (ø)

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@glemaitre glemaitre merged commit 4dfd35c into scikit-learn-contrib:master Sep 6, 2018
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Successfully merging this pull request may close these issues.

Create a class BalancedRandomForestClassifier
4 participants