feat: handling unseen categories in glm h2o architectures #81
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vishpillai123
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GLM architectures in H2O (aka "Generalized Linear Models") have a number of differences compared to our other architectures DRT/XRT, GBM, and XGBoost (all of which are tree based models). These architectures struggle with unseen categories in enum variables ("term_program_of_study"), for example.
The best way to manage this is to impute categorical variables prior to running SHAP, otherwise we run into hard errors in H2O. I built a helper for GLM case that imputes with mode. We also log all of this prior to running SHAP.
We really should move away from high cardinality categoricals, as that's what creates the most risk. Will work on this separately.