https://github.com/onelearn/onelearn

Online machine learning methods
https://github.com/onelearn/onelearn

classification machine-learning online-learning-algorithms python random-forest regression

Last synced: 5 months ago
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Online machine learning methods

• Host: GitHub
• URL: https://github.com/onelearn/onelearn
• Owner: onelearn
• License: bsd-3-clause
• Created: 2020-03-05T15:59:53.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-09-29T14:40:31.000Z (over 4 years ago)
• Last Synced: 2025-09-25T14:17:46.559Z (9 months ago)
• Topics: classification, machine-learning, online-learning-algorithms, python, random-forest, regression
• Language: Python
• Homepage: https://onelearn.readthedocs.io
• Size: 12.1 MB
• Stars: 21
• Watchers: 3
• Forks: 3
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# onelearn: Online learning in Python

[Documentation](https://onelearn.readthedocs.io) | [Reproduce experiments](https://onelearn.readthedocs.io/en/latest/experiments.html) |

onelearn stands for ONE-shot LEARNning. It is a small python package for **online learning** 

with Python. It provides :

- **online** (or **one-shot**) learning algorithms: each sample is processed **once**, only a 

  single pass is performed on the data

- including **multi-class classification** and regression algorithms

- For now, only *ensemble* methods, namely **Random Forests**

## Installation

The easiest way to install onelearn is using pip

    pip install onelearn

But you can also use the latest development from github directly with

    pip install git+https://github.com/onelearn/onelearn.git

## References

    @article{mourtada2019amf,

      title={AMF: Aggregated Mondrian Forests for Online Learning},

      author={Mourtada, Jaouad and Ga{\"\i}ffas, St{\'e}phane and Scornet, Erwan},

      journal={arXiv preprint arXiv:1906.10529},

      year={2019}

    }