BARMPy: Bayesian Additive Regression Models Python Package
Fuente:
arXiv
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917632988413952 |
|---|---|
| author | Van Boxel, Danielle |
| author_facet | Van Boxel, Danielle |
| contents | We make Bayesian Additive Regression Networks (BARN) available as a Python package, \texttt{barmpy}, with documentation at \url{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 \texttt{barmpy}, we produce a companion tutorial that expands on reference information in the documentation. Any interested user can \texttt{pip install barmpy} from the official PyPi repository. \texttt{barmpy} also serves as a baseline Python library for generic Bayesian Additive Regression Models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04738 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BARMPy: Bayesian Additive Regression Models Python Package Van Boxel, Danielle Computation Machine Learning We make Bayesian Additive Regression Networks (BARN) available as a Python package, \texttt{barmpy}, with documentation at \url{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 \texttt{barmpy}, we produce a companion tutorial that expands on reference information in the documentation. Any interested user can \texttt{pip install barmpy} from the official PyPi repository. \texttt{barmpy} also serves as a baseline Python library for generic Bayesian Additive Regression Models. |
| title | BARMPy: Bayesian Additive Regression Models Python Package |
| topic | Computation Machine Learning |
| url | https://arxiv.org/abs/2404.04738 |