River: Machine learning for streaming data in Python
| dc.contributor.author | Montiel, Jacob | en_NZ |
| dc.contributor.author | Halford, Max | en_NZ |
| dc.contributor.author | Mastelini, Saulo Martiello | en_NZ |
| dc.contributor.author | Bolmier, Geoffrey | en_NZ |
| dc.contributor.author | Sourty, Raphael | en_NZ |
| dc.contributor.author | Vaysse, Robin | en_NZ |
| dc.contributor.author | Zouitine, Adil | en_NZ |
| dc.contributor.author | Gomes, Heitor Murilo | en_NZ |
| dc.contributor.author | Read, Jesse | en_NZ |
| dc.contributor.author | Abdessalem, Talel | en_NZ |
| dc.contributor.author | Bifet, Albert | en_NZ |
| dc.date.accessioned | 2021-06-23T02:46:18Z | |
| dc.date.available | 2021-06-23T02:46:18Z | |
| dc.date.issued | 2021 | en_NZ |
| dc.description.abstract | River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of two popular packages for stream learning in Python: Creme and scikit- multiow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River's ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same um-brella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river. | en_NZ |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Montiel, J., Halford, M., Mastelini, S. M., Bolmier, G., Sourty, R., Vaysse, R., … Bifet, A. (2021). River: Machine learning for streaming data in Python. Journal of Machine Learning Research, 22(10), 1–8. | en |
| dc.identifier.eissn | 1533-7928 | en_NZ |
| dc.identifier.issn | 1532-4435 | en_NZ |
| dc.identifier.uri | https://hdl.handle.net/10289/14402 | |
| dc.language.iso | en | |
| dc.relation.isPartOf | Journal of Machine Learning Research | en_NZ |
| dc.relation.uri | http://jmlr.org/papers/v22/20-1380.html | en_NZ |
| dc.rights | © 2021 Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet. License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v22/20-1380.html. | |
| dc.subject | stream learning | en_NZ |
| dc.subject | online learning | en_NZ |
| dc.subject | data stream | en_NZ |
| dc.subject | concept drift | en_NZ |
| dc.subject | supervised learning | en_NZ |
| dc.subject | unsupervised learning | en_NZ |
| dc.subject | Python | en_NZ |
| dc.subject | computer science | en_NZ |
| dc.subject | Machine learning | |
| dc.title | River: Machine learning for streaming data in Python | en_NZ |
| dc.type | Journal Article | |
| dspace.entity.type | Publication | |
| pubs.begin-page | 1 | |
| pubs.end-page | 8 | |
| pubs.issue | 10 | en_NZ |
| pubs.publication-status | Published | en_NZ |
| pubs.volume | 22 | en_NZ |