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enhancement (something similar to randomForest::na.roughfix) #4
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additional thoughts on this... I don't see in
That might be an adequate solution ? |
Thanks for your comments, which are always very welcome. The rangers are still evaluating the best method to allow for missing values in predictors, I am waiting for this already quite a bit ;). No panic about the Let me demonstrate with a data set without any complete row:
Personally, I use |
Hi Michael,
I don't know what's your target audience/users, but currently in
missRanger::missRanger
(line 67):you're using
fit <- ranger::ranger(stats::reformulate(completed, response = v)
wherecompleted
requires columns to have no missing data. This behaviour for some dataset (e.g. genomic) can create huge bias by drastically reducing the number of available variables for training.I suggest an enhancement similar to
randomForest::na.roughfix
where an additional argument would give user the possibility of quickly filling missing values of predictor/training set columns.Also discussed here: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#missing1
Cheers
Thierry
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