RCProb is a probabilistic extension of rule extraction from tree ensembles that improves probability estimates by using smoothed atomic class-conditional evidence and a support‑adaptive mixture for final rule probabilities. Compared to RuleCOSI+, RCProb reduces median paired log‑loss by 71.9% for random forests and 62.5% for gradient boosting, while also decreasing the number of extracted rules by about 38% for both ensemble types. The method shows significant improvements in calibration metrics such as Confidence‑ECE and competitive native probability estimates, with further gains possible through post‑hoc calibration.
By Josue Obregon
arXiv:2609.24528v1 Announce Type: cross
Abstract: Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs...
By Lea L. Mairh\"ofer, Silke Szymczak, Bj\"orn-Hergen Laabs, Tuwe L\"ofstr\"om-Cavallin
arXiv:2609.26839v1 Announce Type: cross
Abstract: Post-hoc probability calibration is usually evaluated under an optimistic assumption: the held-out calibration labels are clean. In many AI deploymen...
By Zeming Liu, Hang Lyu, Jingtao Zhang, Yuan Xie
Hyperparameter optimization (HPO) for Random Forest faces a specific difficulty in tuning the number of trees: the predictive score typically improves monotonically with ensemble size, so standard methods such as Tree-structured Parzen Estimator (TPE) and Hyperband require a predefined search range and often drive the estimate toward its right boundary. Early-stopping strategies avoid fixing such a range, but can be sensitive to score noise and prone to premature stopping.
arXiv:2606. 30995v1 Announce Type: new Abstract: Recent work has shown that well-optimized individual decision trees can match complex black box models in some settings, primarily in noisy domains.
By Zakk Heile, Hayden McTavish, Margo Seltzer, Cynthia Rudin
Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs) were developed as interpretable surrogate models...