Partition Tree: Conditional Density Estimation over General Outcome Spaces

ArXi:2602.04042v2 Announce Type: replace We propose Partition Tree, a novel tree-based framework for conditional density estimation over general outcome spaces that s both continuous and categorical variables within a unified formulation. Our approach models conditional distributions as piecewise-constant densities on data-adaptive partitions and learns trees by directly minimizing conditional negative log-likelihood. This yields a scalable, nonparametric alternative to existing probabilistic trees that does not make parametric assumptions about the target distribution. We further.