A question on h2o mojo models.
Is my understanding correct that GLM MOJO models don't hold variable importance for the model?
Or is there something that am missing?
I get the below message on screenshot sometimes when I query varimp/varimp_plot from the GLM model.
"Warning: This model doesn't have variable importance."
Is this usual? while we get varimp from the same model within the kernel which generated them. Just curious to understand this.
Any leads would be much appreciated.
MOJO models are H2O's primary way of taking models into production. These self-contained zip files are primarily meant to be run via genmodel and not inspected. MOJO model does not equal binary model, which is tied to a certain H2O version. The reason for that is simple - algorithm parameters and the algorithm itself may change between versions.
Anyway, H2O provides a way to import MOJOs back into H2O and primarily use them for scoring. Some attributes of MOJOs are still extracted from the MOJO and provided to the user. But, as the documentation says, it is not guaranteed which model parameters are exposed and some might be missing. MOJO model import is implemented as a part of H2O's Generic model functionality - the ability of H2O to "embrace" any model, even the ones trained outside H2O, provided the "Generic model driver" is available.
With that said, there is definitely a way to provide variable importances to MOJO import functionality users. This is a known problem already and is tracked in H2O JIRA.
More resources on MOJO model on my blog.
