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.
arXiv:2605. 13830v2 Announce Type: replace-cross Abstract: Decision tree ensembles (DTE) are a popular model for a wide range of AI classification tasks, used in multiple safety critical domains, and hence verifying properties on these models has been an active topic of study over the last decade.
arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.
arXiv:2602. 07453v2 Announce Type: replace Abstract: Decision tree ensembles are widely used in critical domains, making robustness and sensitivity analysis essential to their trustworthiness.
arXiv:2607. 28170v1 Announce Type: new Abstract: Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging.
arXiv:2608. 15725v1 Announce Type: new Abstract: Predictive models in clinical and regulated settings must be accurate and fully auditable.
arXiv:2607. 27522v1 Announce Type: new Abstract: As decision-making processes grow more complex, machine learning tools have become essential for tackling business and societal challenges.
arXiv:2605. 20716v5 Announce Type: replace Abstract: Random forests construct each tree with a different, randomised representation of the feature space.
arXiv:2605. 30119v2 Announce Type: replace-cross Abstract: Survival analysis concerns the task of predicting the time until an event occurs.
arXiv:2608. 04310v1 Announce Type: new Abstract: The Rashomon effect describes the phenomenon that many models can achieve nearly equivalent performance on the same learning task, with significant ramifications for robustness, feature importance, and customizability.
arXiv:2606. 00202v1 Announce Type: cross Abstract: Standard machine learning pipelines often admit many near-optimal models.
arXiv:2512. 22240v5 Announce Type: replace-cross Abstract: Machine learning models are primarily judged by predictive performance, especially in applied genomics, where explanations are read as biological findings.
arXiv:2608. 03111v1 Announce Type: new Abstract: Double descent is commonly studied by scaling an explicit capacity parameter, such as neural-network width.