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This will add functionality to handle missing values encoded as
numpy.NaN
.The proposal is to classify the masked values (excluding NaNs), and then set the bin label for the missing observations to -1.
This would keep the labels as ints, rather than using
numpy.NaN
which would change the labels to floats.This is a work-in progress PR to solicit comments and ideas.