Hi, you reminded me, I just saw (not yet registered, is in the General queue; can still be used):
This package implements the Stable and Interpretable RUle Sets (SIRUS) for classification. Regression is also technically possible but not yet implemented.
The SIRUS algorithm was presented by Bénard et al. in 2020 and 2021. In short, SIRUS combines the predictive accuracy of random forests with the explainability of decision trees while remaining stable. […]
Intriguing, but not too helpful since you’re looking for regression (you can look up if available already through e.g. Python/SciKitLearn?).
I just googled a bit, and you can see how RandomForestRegressor is used here: https://github.com/cstjean/ScikitLearn.jl/blob/master/examples/Decision_Tree_Regression_Julia.ipynb
and RandomizedSearchCV here (though with a classifier, I’m unfamiliar with this but since also works with regression according to SciKitLearn’s docs, should also be workable from Julia): https://github.com/cstjean/ScikitLearn.jl/blob/master/examples/Randomized_Search.ipynb
I’m more curious myself if possible, and how used without SciKitlearn (while very viable to use with Python code):
I did find this code (for pre-1.0 Julia):
and this for current: