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Abstract/Summary
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This paper introduces arfpy, a python implementation of Adversarial Random Forests, which is a lightweight procedure for synthesizing new data that resembles some given data. The software 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, 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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