Can you provide a summary of what exactly is left to do in the PR description? Thanks!

@massich check out my branch feature/balanced-random-forest-api. The changes are:

1. Followed the discussion of @glemaitre @arjoly @amueller in bootstrapping based on sample weights in random forests #8607 to remove the ad-hoc support for multioutput balanced randomf forest and raising an error when it is attempted.

2. Added unit tests for the two BRF helper methods to test_balanced_random_forest.py-- it wasn't obvious to me which of the existing test files they belong in so feel free to move them.

3. I changed the API to be class_weight="balanced_bootstrap" as discussed in bootstrapping based on sample weights in random forests #8607.

Please let me know what is left to get this merged.

@potash I am benchmarking the estimator here. My idea for the benchmark is:

• Using sklearn datasets:
  • Create a synthetic dataset and go from balanced to highly unbalanced to see when BRF is beneficial
  • Repeat the experiment with Breast dataset in Sk-learn.
• Using sklearn-imbalance:
  • Test against their selection of imbalanced datasets
• Using openML:
  • Explore some imbalanced datasets

Sounds good. You'll want to merge feature/balanced-random-forest-api so you can work off the new api (class_weight="balanced_bootstrap") and merge brf-example as it's been updated there too. Let me know if I can help with the examples.

There's some benchmarks here on a real datasets and also a silly implementation of the feature using imblearn: https://github.com/amueller/applied_ml_spring_2017/blob/master/slides/aml-15-resampling-imbalanced-data.ipynb
You can see round Out[83] that this method is doing much better than any of the others.

Hello there, is it possible to get an update on this? We're using this model in production (https://github.com/Chicago/lead-model), and as we prepare to go live it would be very helpful for deployment if this branch were in the standard sci-kit learn library.

Thanks for all the great work here!

Also, let us know if there's something we can do to move this forward.

this needs tests, documentation and examples. I'm a big fan of this methods, so I'd be happy to see this moved forward. @massich are you still working on it? Would you like some help?
I liked using the mammography dataset: https://www.openml.org/d/310, see #9908 for a loader ;)

In the meanwhile, we have the BalancedBaggingClassifier which can be set to a balanced random forest by setting max_features='auto' if I am not wrong.

@glemaitre I believe you are right.

Actually, it completely stalled. I did not even finish the benchmark. I was playing with openml but I didn't finish it. It has been sitting for 6 months.

We should definitely revive it.

@massich what is the current status of this PR? Do you need a hand? According to a previous comment of @amueller this PR needs love, tests, documentation and examples, right?

IMO it would be good if you helped complete this, @chkoar

@jnothman That was the intention. If it is not picked by anyone else I will give it a in a couple of weeks. @massich has already given write access to me on his repos

@chkoar let me know if there's anything I (original author of the feature) can do to help. Would be very happy to see this merged.

@potash ok, thanks. Let's hope that it will be merged during the upcoming sprint.

I think you should expect a little less. But let's honours list hope it will be a lot closer to merge after the sprint.

closing in favor of #13227. Thx @chkoar for taking over.