发表机构
University of Luebeck; University Medical Center Göttingen; Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine; Jönköping University（吕贝克大学; 哥廷根大学医学中心; 下萨克森州医学人工智能与因果方法中心; 延雪平大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将人工代表树与Mondrian共形预测系统结合，提出一种兼具可解释性和稳定性的单一预测模型，可提供连续预测、预测区间和概率，在多个数据集上验证了其性能与可重复性。
AI 中文摘要
随机森林（RFs）预测性能良好但缺乏可解释性，而单一决策树可解释但稳定性差。人工代表树（ARTs）被开发为随机森林的可解释替代模型，但其作为带不确定性量化的独立预测模型的用途尚未被系统研究。我们将ARTs与叶级Mondrian共形预测系统（CPS）相结合，使单棵树能够提供连续预测、预测区间以及超过任意阈值的概率。我们在五种模拟场景、21个基准数据集和一个横截面NHANES示例数据集上，将带CPS的ARTs与带CPS的决策树以及分离的回归树和概率树进行了比较。重复交叉验证评估了预测性能、可解释性和稳定性。带CPS的ARTs生成紧凑、结构稳定的树，在基准数据集和NHANES上，其分裂变量选择的可重复性显著高于决策树。决策树显示出略好的预测性能和更窄的预测区间，而覆盖率大致相当。基于CPS的树通常比多模型方法获得更低且变异更小的Brier分数。因此，将ARTs与CPS相结合，为连续预测和校准概率提供了一个单一、可解释且稳定的模型，在稳定性和可解释性至关重要的场景中，平衡了预测性能与可重复性和透明度。
英文摘要
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 for RFs, but their use as standalone prediction models with uncertainty quantification has not been systematically investigated. We combine ARTs with leaf-wise Mondrian conformal predictive systems (CPS), enabling a single tree to provide continuous predictions, prediction intervals, and probabilities of exceeding arbitrary thresholds. We compared ARTs with CPS against decision trees with CPS and separate regression and probability trees across five simulation scenarios, 21 benchmark datasets, and a cross-sectional NHANES example data set. Repeated cross-validation assessed predictive performance, interpretability, and stability. ARTs with CPS yield compact, structurally stable trees with substantially more reproducible split-variable selection than decision trees across benchmark datasets and NHANES. Decision trees showed slightly better predictive performance and narrower prediction intervals, while coverage was broadly comparable. CPS-based trees generally achieved lower and less variable Brier scores than multi-model approaches. Combining ARTs with CPS therefore provides a single, interpretable, and stable model for continuous predictions and calibrated probabilities, balancing predictive performance with reproducibility and transparency in settings where stability and interpretability are essential.
Comments28 pages and 10 figures (without appendix)
