Handling intentionally missing data with tree-based methods I am using a tree-based method (specifically, random forest) to model the quality of sunsets based on weather measurements. One feature available is the height of the clouds. When there are no clouds the data is set to 99999. It's my impression that keeping the values at 99999 (or setting them to 0 or -999) will bias the predictions, as a tree will consider the 99999 real physical values when they should really be effectively ignored. I've considered adding a dummy variable to indicate whether there are clouds or not, but if I want to include cloud height, which I think could be relevant to the quality of sunsets, I feel like I'll need to do something with the 99999s. Is there an accepted way of handling this type of intentionally missing data with tree-based methods?
I've found a few questions related to this issue, but none have a solution to my problem:
Dummy variable method for missing data in ML/predictive models
How to deal with intentionally missing data
How should I define missing values due to skip questions in SPSS?
 A: Many tree model implementations treat missing values separately: choose an optimal split among the non-missing values, then decide which path the missing values should go with.  That gives the greatest flexibility, which may or may not be best, depending on the bias-variance tradeoff in the rest of your setup.
Note too that tree models (except for extremely-randomized trees) don't take into account the scale of the variables at all.  All that matters is that 99999 is larger than all the other values for the feature.  So using 99999 or -999 just enforce that those will be treated similarly to other large or small (resp.) values, rather than as either depending on the node as NAs would be treated.  In your context, keeping 99999 might make sense: sufficiently high clouds aren't really in the way of a sunset?
See also:
(DS.SE) What is the difference between filling missing values with 0 or any othe constant term like -999?
How do decision tree learning algorithms deal with missing values (under the hood)
