Comments (4)
Hi Chandan, it's great to have your comment. The imodels package is brilliant and it has a very good coverage of rule-based interpretable models! We definitely would like to give it a try to include RuleFit into the PiML toolbox. -- Aijun
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@ajzhanghk we should put FIGS in PiML.
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@csinva Thanks for your great work on FIGS, a very neat version of interpretable rules. We have re-engineered FIGS with much faster speed and integrated it into PiML V0.4.0. Please try it out and let us know your thoughts.
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Thanks for including it! I've been playing with it and it's working really well 😄 I'll be recommending PiML to all the interpretability folks I know!
P.S. If you found FIGS useful, I think you might also want to add HSTree. It's easier to implement, faster, and when we were benchmarking interpretable methods in autogluon, it consistently performed at the top.
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Related Issues (20)
- Colab cannot import piml: initialization of _internal failed HOT 3
- VSCode cannot display some graphs HOT 2
- Trouble Registering a Tensorflow Model HOT 7
- Example codes do not run on Colab HOT 2
- GAMI-Tree implementation
- Where are the source codes HOT 1
- exp.data_prepare() not working HOT 1
- Link to OpenTeams Videos somewhere here HOT 3
- Consider model ranking HOT 1
- High-code grid search examples? HOT 2
- Support residual by level or bin of feature? HOT 1
- Support Spearman correlation? HOT 2
- Elasticnet inconsistencies HOT 3
- scikit-learn 1.3.0 triggers "ImportError: cannot import name SCORERS" HOT 1
- Python 3.11 HOT 1
- Handling Larger size datasets HOT 1
- Error in data_prepare HOT 1
- Very slow import HOT 1
- Can we enable conda forge for PiML
- Possibly documentation error? HOT 1
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