Hi there,
I have played a bit with DecisionTree.jl and MJL.jl.
Now I would like to get some uncertainty quantification for my RandomForest regressions. I have seen that Quantile Random Forest is an increasingly accepted method to get the prediction intervals 5%-95% percentiles over the learner trees instead of only the mean like in standard RF regression. Does someone have a pointer how to go about this in Julia? There are some Python implementations I am aware of like:
- sklearn-quantile · PyPI
- Quantile Regression Forests - Scikit-garden
- GitHub - zillow/quantile-forest: Quantile Regression Forests compatible with scikit-learn.
I don’t think that I am skilled enough to port these myself, but I’d be happy to help in some way.