enhancement (something similar to randomForest::na.roughfix) about missranger HOT 2 CLOSED

additional thoughts on this...

I don't see in ranger's code anywhere how they handle missing in predictor, I guess they don't, but for the genomic example using GWAS data they say (line 70 in ranger::ranger):

Note that missing values are treated as an extra category while splitting

That might be an adequate solution ?

from missranger.

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 completed columns in missRanger: It might be empty at the beginning of the iterative procedure and is then built up step by step in the first iteration.

Let me demonstrate with a data set without any complete row:

# input
mydat <- data.frame(x = c(NA, NA, 1), y = c(NA, 2, NA))
mydat
missRanger(mydat)

# output
  x y
1 1 2
2 1 2
3 1 2

Personally, I use missRanger usually after logical imputations. So for instance if I have a column with only "x" and a lot of NA (as we typically have with tickbox data), then I manually replace the NA by "Not ticked" (or just use a dummy being 1 if "x" and 0 else). In certain applications, it makes sense to replace all categorical variables by a new category like "none", but not always. At the moment I am evaluating different ways how to further develop missRanger. An idea would be to add an option minPropForNone = 1, which would replace all missing values in categorical factors with more than minPropForNone missings by "none". In a next step, we could add similar rules for highly discrete numeric columns.

from missranger.