BARMPy: Bayesian additive regression models Python package

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-08-04 DOI:10.1007/s00180-024-01535-9

Danielle Van Boxel

引用次数: 0

Abstract

We make Bayesian additive regression networks (BARN) available as a Python package, barmpy, with documentation at https://dvbuntu.github.io/barmpy/ for general machine learning practitioners. Our object-oriented design is compatible with SciKit-Learn, allowing usage of their tools like cross-validation. To ease learning to use barmpy, we produce a companion tutorial that expands on reference information in the documentation. Any interested user can pip install barmpy from the official PyPi repository. barmpy also serves as a baseline Python library for generic Bayesian additive regression models.

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BARMPy：贝叶斯加性回归模型 Python 软件包

我们将贝叶斯加性回归网络（BARN）作为一个 Python 软件包（barmpy）提供给广大机器学习从业者，其文档请访问 https://dvbuntu.github.io/barmpy/。我们面向对象的设计与 SciKit-Learn 兼容，允许使用交叉验证等工具。为了方便学习使用 barmpy，我们编写了配套教程，对文档中的参考信息进行了扩展。任何感兴趣的用户都可以从官方 PyPi 代码库中 pip 安装 barmpy。barmpy 还是通用贝叶斯加法回归模型的 Python 基线库。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

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