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arXiv · 2311.07366

arfpy: A python package for density estimation and generative modeling with adversarial random forests

Kristin Blesch·Marvin N. Wright
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Abstract

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.

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BibTeXRIS

Kristin Blesch, Marvin N. Wright. 2023-11-13. arfpy: A python package for density estimation and generative modeling with adversarial random forests. https://arxiv.org/abs/2311.07366

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