What is the best approach to include a zero-inflated continuous independent feature (e.g., 90% of the values are Zero, 10% are >0) in a Tree-based models (DT, random forest, gradient boosting. etc). I am considering the following three options:
Option 1: Keep the zero-inflated continues feature as-is.
Option 2: Replace the continuous feature with a binary interaction feature (i.e., 0 for X=0; 1 for X>0)
Option 3: Include both the continuous and categorical features.
The main justification I have for Option 1 is that the continuous feature can be used in more than one split. I am also aware that option 3 indicates including two highly correlated independent features. Will I be losing information if I use option 2?
Update: I found the following answer; however, I am not sure if it can be generalized for tree-based models