arXiv Machine Learning September 3, 2026 By Josue Obregon

RCProb: Probabilistic rule extraction from classification tree ensembles

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

safety

arXiv Machine Learning

Jun 2

Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification

arXiv:2605. 20716v5 Announce Type: replace Abstract: Random forests construct each tree with a different, randomised representation of the feature space.

By Youngjoon Park

benchmarks safety

arXiv Machine Learning

Aug 18

Learning Auditable Classifier Models: Source-Disjoint Tree Ensembles

arXiv:2608. 15725v1 Announce Type: new Abstract: Predictive models in clinical and regulated settings must be accurate and fully auditable.

By Srikumar Krishnamoorthy

benchmarks

arXiv Machine Learning

Jun 30

The Hidden Cost of Resampling: How Imbalance Correction Degrades Probability Calibration in Tree Ensembles

arXiv:2606. 29720v1 Announce Type: new Abstract: Resampling methods such as SMOTE and random under/over-sampling are standard tools for class-imbalanced classification, almost always evaluated by minority-class accuracy or F1.

By Zewen Liu

arXiv Machine Learning

Jul 30

TreeCCA: Canonical Correlation Analysis via Gradient-Boosted Trees

arXiv:2607. 27027v1 Announce Type: new Abstract: Gradient-boosted trees dominate tabular machine learning, yet canonical correlation analysis has always relied on linear or neural encoders.

By James Chapman

benchmarks safety

arXiv AI

Aug 7

Evidential Rule Learning for Interpretable Classification with Abstention

arXiv:2608. 05859v1 Announce Type: cross Abstract: Interpretable classification often requires more than accurate predictions for real-life deployment: models should be transparent about the evidence behind their decisions and abstain when they cannot decide reliably.

By Javier Fumanal-Idocin, Javier Andreu-Perez

benchmarks

arXiv Machine Learning

Jul 1

Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again

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

benchmarks safety

One email a morning, machine-written

Related stories

Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification

Learning Auditable Classifier Models: Source-Disjoint Tree Ensembles

The Hidden Cost of Resampling: How Imbalance Correction Degrades Probability Calibration in Tree Ensembles

TreeCCA: Canonical Correlation Analysis via Gradient-Boosted Trees

Evidential Rule Learning for Interpretable Classification with Abstention

Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again