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Found 4 papers, 1 papers with code
MMD-based Variable Importance for Distributional Random Forest
Distributional Random Forest (DRF) is a flexible forest-based method to estimate the full conditional distribution of a multivariate output of interest given input variables.
Confidence and Uncertainty Assessment for Distributional Random Forests
The Distributional Random Forest (DRF) is a recently introduced Random Forest algorithm to estimate multivariate conditional distributions.
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression
Random Forest (Breiman, 2001) is a successful and widely used regression and classification algorithm.
On the Use of Random Forest for Two-Sample Testing
The developed tests are easy to use, require almost no tuning, and are applicable for any distribution on $\mathbb{R}^d$.
Methodology
