https://github.com/aimclub/FEDOT

Automated modeling and machine learning framework FEDOT
https://github.com/aimclub/FEDOT

automated-machine-learning automation automl evolutionary-algorithms fedot genetic-programming hyperparameter-optimization machine-learning multimodality parameter-tuning structural-learning

Last synced: 2 months ago
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Automated modeling and machine learning framework FEDOT

• Host: GitHub
• URL: https://github.com/aimclub/FEDOT
• Owner: aimclub
• License: bsd-3-clause
• Created: 2020-01-13T12:48:37.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2024-10-29T19:40:44.000Z (3 months ago)
• Last Synced: 2024-10-29T21:40:33.054Z (3 months ago)
• Topics: automated-machine-learning, automation, automl, evolutionary-algorithms, fedot, genetic-programming, hyperparameter-optimization, machine-learning, multimodality, parameter-tuning, structural-learning
• Language: Python
• Homepage: https://fedot.readthedocs.io
• Size: 224 MB
• Stars: 642
• Watchers: 11
• Forks: 88
• Open Issues: 78
• Metadata Files:
  • Readme: .github/README.rst
  • Contributing: .github/CONTRIBUTING.md
  • License: LICENSE.md
  • Code of conduct: .github/CODE_OF_CONDUCT.md

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• awesome-llmops - FEDOT - itmo/FEDOT.svg?style=flat-square) | (AutoML / Profiling)
• StarryDivineSky - aimclub/FEDOT - Clause BSD许可证发布。FEDOT可以自动生成机器学习管道，用于解决各种现实世界问题，包括分类（二元和多元）、回归、聚类和时间序列预测。FEDOT的核心基于进化方法，它允许用户自定义管道，并支持各种模型和数据类型，包括文本、图像和表格数据。此外，FEDOT还支持广泛使用的机器学习库（如Scikit-learn、CatBoost、XGBoost等），并允许用户集成自定义模型。FEDOT提供了多种超参数调优方法，并支持自定义评估指标和搜索空间。FEDOT不仅限于特定的建模任务，还可以用于解决常微分方程（ODE）或偏微分方程（PDE）等问题。用户可以将生成的管道导出为JSON格式，或与输入数据一起打包为ZIP存档，以确保实验的可重复性。 (参数优化)