We propose Partition Tree, a tree-based framework for conditional density estimation over general outcome spaces that supports 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 greedily minimizing a surrogate for conditional log-loss. This yields a scalable alternative to existing probabilistic trees that does not make parametric assumptions about the target distribution. We further introduce Partition Forest, an ensemble extension obtained by averaging conditional densities. Empirically, we demonstrate improved probabilistic prediction over CART-style trees and competitive performance compared to state-of-the-art probabilistic tree methods and Random Forests.

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