born-again-tree-recovers-interpretability

IN premise — entries/2026/06/21/wiki-Random_forest-chunk-4.md

Created 2026-06-21T09:55:52+00:00

Model compression via 'born-again' decision trees can transform a random forest into a single minimal decision tree that faithfully reproduces the ensemble's decision function, recovering interpretability.

Dependents

These beliefs depend on this one:

• IN born-again-trees-prove-interpretability-extractable-but-not-scalable — Born-again decision trees demonstrate that interpretability can be extracted from black-box ensembles — yet this extraction path leads back to the interpretable model families whose inverse correlation with capability is already established, proving that interpretability recovery is possible in principle but constrained to the same capability ceiling that makes interpretable models insufficient.