General OOD detection via model-aware and subspace-aware variable priority.

Out-of-distribution (OOD) detection flags test inputs that depart from the data used to train a model. For structured tabular problems with regression or survival outcomes, existing methods remain limited because many OOD detectors are designed for classification or unstructured data. We introduce a tree based method that uses the rule structure of a supervised forest to determine the predictive subspace, the model-aware reference neighborhood, and the final OOD score in a single construction. The score compares each test input with forest selected reference cases only on prediction relevant variables, reducing signal dilution from nuisance coordinates. Across synthetic and real data benchmarks, the method performs especially well for subtle targeted feature shifts and changes in dependence. An esophageal cancer survival study further shows how OOD scores can reveal lymphadenectomy related shifts relevant to surgical guidelines.