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lukesonnet / KRLS

R package for machine learning technique to fit flexible, interpretable functional forms for continuous and binary outcomes.
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Dealing with missingness #3

Open chadhazlett opened 7 years ago

chadhazlett commented 7 years ago

Ideally we'd deal with missingness much as lm does -- i.e. it is na.omitted internally but then the predictions etc. are put back into the full length vector with the NA intermixed.

lukesonnet commented 7 years ago

What about in the returned K, U, etc?

chadhazlett commented 7 years ago

I would propose we leave out the missingness in those objects and just keep NAs in all the fit/predict type objects. Main motive for this would be that if people are generating predictions by multiple procedures then it needs to be easy to weave them together.