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A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
| Date | Stars |
|---|---|
| 2026-07-31 | 10050 |
| 2026-08-01 | 10050 |
| 2026-08-06 | 10047 |
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# TPOT
<center>
<img src="https://raw.githubusercontent.com/EpistasisLab/tpot/master/images/tpot-logo.jpg" width=300 />
</center>
<br>
[](https://pypi.org/project/TPOT)
[](https://anaconda.org/conda-forge/tpot)
TPOT stands for Tree-based Pipeline Optimization Tool. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. Consider TPOT your Data Science Assistant.
## Contributors
TPOT recently went through a major refactoring. The package was rewritten from scratch to improve efficiency and performance, support new features, and fix numerous bugs. New features include genetic feature selection, a significantly expanded and more flexible method of defining search spaces, multi-objective optimization, a more modular framework allowing for easier customization of the evolutionary algorithm, and more. While in development, this new version was referred to as "TPOT2" but we have now merged what was once TPOT2 into the main TPOT package. You can learn more about this new version of TPOT in our GPTP paper titled "TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning."
Ribeiro, P. et al. (2024). TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning. In: Winkler, S., Trujillo, L., Ofria, C., Hu, T. (eds) Genetic Programming Theory and Practice XX. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-8413-8_1
The current version of TPOT was developed at Cedars-Sinai by:
- Pedro Henrique Ribeiro (Lead developer - https://github.com/perib, https://www.linkedin.com/in/pedro-ribeiro/)
- Anil Saini ([email protected])
- Jose Hernandez ([email protected])
- Jay Moran ([email protected])
- Nicholas Matsumoto ([email protected])
- Hyunjun Choi ([email protected])
- Gabriel Ketron ([email protected])
- Miguel E. Hernandez ([email protected])
- Jason Moore ([email protected])
The original version of TPOT was primarily developed at the University of Pennsylvania by:
- Randal S. Olson ([email protected])
- Weixuan Fu ([email protected])
- Daniel Angell ([email protected])
- Jason Moore ([email protected])
- and many more generous open-source contributors
## License
Please see the [repository license](https://github.com/EpistasisLab/tpot/blob/main/LICENSE) for the licensing and usage information for TPOT.
Generally, we have licensed TPOT to make it as widely usable as possible.
TPOT is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as
published by the Free Software Foundation, either version 3 of
the License, or (at your option) any later version.
TPOT is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with TPOT. If not, see <http://www.gnu.org/licenses/>.
## Documentation
[The documentation webpage can be found here.](https://epistasislab.github.io/tpot/)
We also recommend looking at the Tutorials folder for jupyter notebooks with examples and guides.
## Installation
TPOT requires a working installation of Python.
### Creating a conda environment (optional)
We recommend using conda environments for installing TPOT, though it would work equally well if manually installed without it.
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:af282dd56dafb327, topic:automation