Beyond Point Prediction: Artificial Representative Trees with Uncertainty

Data and Models for Reproducibility This repository contains the models and results from the paper: Kronziel et al. (2026) “Beyond Point Prediction: Artificial Representative Trees with Uncertainty” The upload includes three zipped data collections corresponding to: the simulation study the benchmark experiment the application to the NHANES dataset Together with the R code provided in the associated GitHub repository (https://github.com/imbs-hl/ART_uncertainty_paper) all figures from the publication can be fully reproduced. In addition, users can simulate their own data and run custom experiments. Contents of the Zipped Files Each of the three zipped archives contains the following components: Regression modelsFiles with the prefix regression_trees contain the fitted regression prediction models, including Artificial Representative Trees (ARTs) and Decision Trees (DTs) Probability modelsFiles with the prefix probability_trees contain the fitted probability prediction models (ARTs and DTs). For the simulation study and the benchmark experiment, probabilities are predicted with respect to a threshold of 0.5 (mean value). For the NHANES application, two separate files are provided: one for predicting probabilities above the prediabetes threshold (glycohemoglobin ≥ 5.7) one for predicting probabilities above the diabetes threshold (glycohemoglobin ≥ 6.5) ResultsFiles with the prefix results contain data frames with the computed performance metrics. Predicted probabilitiesFiles with the prefix pred_probabilities contain the predicted probabilities per terminal node for ARTs and DTs using conformal predictive systems. Variable usage of random forest (RF)For the NHANES application, an additional CSV file is provided reporting, for each variable in the dataset, how often it was used as a split variable in the corresponding RF (expressed as relative frequency). Reproducibility All results presented in the paper can be reproduced using: the data and model objects provided here, and the R code from the GitHub repository. The setup also enables users to: generate new simulated datasets apply the methods to custom data conduct additional experiments面向可复现研究的数据与模型 本仓库收录了Kronziel等人（2026）发表的论文《超越点预测：带不确定性的人工代表性树》（Beyond Point Prediction: Artificial Representative Trees with Uncertainty）对应的模型与实验结果。 本次上传包含三个压缩数据集集合，分别对应： 1. 仿真研究 2. 基准实验 3. 应用于NHANES数据集的实验 结合关联GitHub仓库（https://github.com/imbs-hl/ART_uncertainty_paper）中提供的R代码，可完全复现论文中的所有图表。此外，用户还可自行生成仿真数据并开展自定义实验。 ## 压缩文件内容 三个压缩归档文件均包含以下组件： ### 回归模型 文件名以`regression_trees`为前缀的文件存储了已拟合的回归预测模型，包括人工代表性树（Artificial Representative Trees, ARTs）与决策树（Decision Trees, DTs）。 ### 概率模型 文件名以`probability_trees`为前缀的文件存储了已拟合的概率预测模型（人工代表性树与决策树）。 对于仿真研究与基准实验，模型以0.5（均值）作为阈值进行概率预测。 针对NHANES数据集的应用场景，我们提供了两组独立文件： 1. 用于预测血糖糖化血红蛋白≥5.7%（糖尿病前期阈值）的概率值 2. 用于预测血糖糖化血红蛋白≥6.5%（糖尿病阈值）的概率值 ### 结果 文件名以`results`为前缀的文件存储了包含已计算性能指标的数据框。 ### 预测概率 文件名以`pred_probabilities`为前缀的文件存储了使用共形预测系统（conformal predictive systems）得到的人工代表性树与决策树各终端节点的预测概率。 ### 随机森林变量使用情况 针对NHANES数据集的应用场景，我们额外提供了一份CSV文件，用于报告数据集中每个变量作为拆分变量在对应随机森林（Random Forest, RF）中被使用的频次（以相对频率表示）。 ## 可复现性说明 论文中呈现的所有结果均可通过以下方式复现： 1. 本文提供的数据与模型对象 2. GitHub仓库中的R代码 本研究框架还支持用户完成以下操作： 1. 生成全新的仿真数据集 2. 将所提方法应用于自定义数据 3. 开展额外的拓展实验