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

• URL: http://arxiv.org/abs/2311.07366v1
• Date: Mon, 13 Nov 2023 14:28:21 GMT
• Title: arfpy: A python package for density estimation and generative modeling with adversarial random forests
• Authors: Kristin Blesch, Marvin N. Wright
• Abstract summary: This paper introduces $textitarfpy$, a python implementation of Adversarial Random Forests (ARF) (Watson et al., 2023) It is a lightweight procedure for synthesizing new data that resembles some given data.
• Score: 1.3597551064547502
• License: http://creativecommons.org/licenses/by/4.0/
• 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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