I was trying out the
FillMissing transformer in
fastai.tabular.transform and noticed that if there are no missing values in a training set column, the transformer will throw this exception when applied to a test set.
I was curious if this behavior was an explicit decision or if any alternative are being considered?
In my particular case, I went ahead and just manually filled the missing test values with the median of the training values without using any
One immediate problem I can see with not halting immediately is that all the
_na columns would be
False in the transformed training set which could lead to unpredictable behavior when doing predictions.
Issues like this may indicate that there’s no good default in general, hence forcing the user to address it, but I figured I’d ask to hear other folks thoughts.