Handling samples with undefined feature values?

[0x7f]

First, thanks for ranger. Keep up the good work!

My issue: I have trouble using ranger with sparse data, i.e. when samples do not have certain continuous variables/features at all. At the moment, I set them to 0.0 but this produces wrong results of course. Checking the code, this is not really possible at the moment, right? First, I was confused by the sparse data feature in the Data class, but it is some feature for the GenABEL library. I mean sparse in the sense of sparse features matrices as in e.g. scipy.

Is this feature planned? If not, could you maybe sketch the solution, so I could help out with a patch?

[0x7f]

Nevermind, I misunderstood how sklearn is handling sparse matrices in the first place. Read the original paper and sklearn's code and it's all about imputation.