InterpretML

Hi @amir78pgd -- This appears to be a question that's better suited for the interpret-community repo. Transferring it there so they can help you. -InterpretML

Hi @epetrovski -- It seems you are using the interpret-community package because TabularExplainer is a class that only exists there. Transferring the issue to them for further response. -InterpretML team

Hi @MassimilianoGrassiDataScience -- Thanks for bringing this up. Ordinal features are currently not supported, but we had some older code left over from testing that you may have run into....

Thanks @RealLucasMeyer! Yes, sample_weight support for EBM is also being tracked in #115 .

(Reposting from issue #62): The latest release of interpret (0.2.5) now has support for sample weights in ExplainableBoostingMachines. You can pass in positive floating point weights to the new `sample_weight`...

Hi @lethaiq , Our apologies for the delay in getting back to you. Congratulations on publishing GRACE at KDD! In the short term, we're focused on revamping a new API...

Hi @onacrame, This is a reasonable suggestion, and one we've been thinking about developing on our end too. The main reason we haven't implemented it yet is due to EBMs...

Hi @JoshuaC3, It's a good question! Right now, we're focused on tree-based models as the base estimators in EBMs. We've found that trees tend to yield the best performance in...

Hi @davidlkl , Thanks for bringing this up! Your use case makes perfect sense, and supporting custom validation sets is on our backlog though we don't have an exact timeline...

Hi @jamie-murdoch, thanks for raising this issue. It's a reasonable feature suggestion, and one that we've been thinking on our side as well. It's good to know it would be...