arfpy：一个用于对抗随机森林密度估计和生成建模的Python包
arfpy: A python package for density estimation and generative modeling with adversarial random forests
AI总结：
本文介绍arfpy，一个Python包，实现了对抗随机森林（ARF）方法，用于密度估计和生成建模，特别适用于表格数据，具有低调参需求和计算资源消耗，并提供用户友好的接口。
摘要：
本文介绍了arfpy，这是对抗随机森林（ARF）（Watson等人，2023）的Python实现，ARF是一种用于合成与给定数据相似的新数据的轻量级方法。arfpy软件为实践者提供了密度估计和生成建模的简便功能。该方法特别适用于表格数据，其竞争性能已在先前文献中得到证明。作为相对于大多数基于深度学习的替代方案的主要优势，arfpy结合了该方法在调参工作和计算资源方面需求减少的特点，并提供了用户友好的Python接口。这为科学领域的受众提供了轻松生成数据的软件。
英文摘要：
This paper introduces $\textit{arfpy}$, a python implementation of Adversarial Random Forests (ARF) (Watson et al., 2023), which is a lightweight procedure for synthesizing new data that resembles some given data. The software $\textit{arfpy}$ equips practitioners with straightforward functionalities for both density estimation and generative modeling. The method is particularly useful for tabular data and its competitive performance is demonstrated in previous literature. As a major advantage over the mostly deep learning based alternatives, $\textit{arfpy}$ combines the method's reduced requirements in tuning efforts and computational resources with a user-friendly python interface. This supplies audiences across scientific fields with software to generate data effortlessly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
