Closed JakeColtman closed 5 years ago
The current approach to selecting feature importance requires generating lots of permutations of the model to produce a null distribution of feature inclusions. At the moment, there's no way to run models in parallel because joblib can't be nested. It would be nice to expose the chains to an external runner, so that all chains from all models can be run at once
Having played around with the current implementation, two things strike:
Still to do - wrap the logic up into an Sklearn BaseFilter
Added to the PR:
Resolution for #26