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TPOT
What TPOT is
TPOT is a free Python automated machine learning tool that uses genetic programming to optimize machine learning pipelines. Its current package includes genetic feature selection, flexible search spaces, multi-objective optimization, and a modular evolutionary algorithm framework. Documentation covers classifiers, regressors, and graph, sequential, tree, and union pipeline types. When preprocessing is enabled, TPOT imputes missing values, one-hot encodes categorical features, and standardizes data. It uses Dask for parallel processing and supports model evaluation and automated machine learning, with API access.
TPOT requires Python and can be installed manually or in a conda environment. The documented Python range is >=3.10 and <3.14, and installation documentation lists scikit-learn, with extra scikit-learn extensions available through the tpot[sklearnex] option. The project began in 2016 and says it was developed at Cedars-Sinai’s Artificial Intelligence Innovation Lab with NIH funding. It is distributed under the GNU Lesser General Public License version 3 or later, without warranty. Documentation warns that input data is not checked for correct formatting and assumes the selected operators can handle it.
Who it suits
TPOT suits Python users looking to automate machine learning pipeline optimization for classification or regression. It may also suit practitioners who want configurable search spaces and multi-objective optimization.
What it does well
- Free software under LGPL version 3 or later.
- Supports classifiers and regressors.
- Offers multiple pipeline types and configurable search spaces.
- Uses Dask for parallel processing.
What to know first
- Requires Python >=3.10 and <3.14.
- Input data formatting is not checked.
- Preprocessing is fitted before cross-validation splitting.
Limits
The documented preprocessing workflow fits and transforms the entire training set before cross-validation splitting; TPOT also assumes that input data is suitable for the chosen operators.
Plans
| Plan | Billed and limits | Price |
|---|---|---|
| TPOT | Free software under LGPL-3.0-or-later | Free |
As epistasislab.github.io lists them, read 9 Oct 2026. The maker's own page has the final word.
Getting started
- Open the TPOT website.
- Install Python in the documented range of 3.10 or later and below 3.14.
- Set up a conda environment or choose a manual installation.
- Install TPOT; use the `tpot[sklearnex]` option for extra scikit-learn extensions.
- Guard parallel script code with an `if __name__ == "__main__"` block when using Dask.
Verdict
TPOT offers a range of pipeline types and preprocessing options for automated machine learning. Users need to prepare suitable input data and account for the documented preprocessing behavior.
Questions
How much does TPOT cost?
TPOT is free software. Its listed plan is free.
Is there a free plan or trial?
The free plan is available. There is no free trial.
Which platforms does TPOT support?
The listed platforms are Linux, macOS, and self-hosted use. TPOT requires Python.
Is TPOT open source?
It is free software distributed under the GNU Lesser General Public License version 3 or later, without warranty.
What does TPOT integrate with?
The installation documentation lists scikit-learn and an optional `tpot[sklearnex]` installation for extra scikit-learn extensions.
How can users get support or report bugs?
The project directs users to GitHub issues to report bugs, suggest enhancements, and discuss extensions.
Facts from TPOT's own pages
- Data handling limit
- TPOT does not check whether input data is correctly formatted and assumes the chosen operators can handle the supplied data. · epistasislab.github.io, 9 Oct 2026
- Features
- The current package includes genetic feature selection, flexible search space definitions, multi-objective optimization, and a modular evolutionary algorithm framework. · epistasislab.github.io, 9 Oct 2026
- Founded
- 2016 · epistasislab.github.io, 28 Sept 2026
- Installation
- TPOT requires Python and can be installed in a conda environment or manually; the documented Python range is >=3.10 and <3.14. · epistasislab.github.io, 9 Oct 2026
- Integrations
- The installation documentation lists scikit-learn and offers extra scikit-learn extensions through the tpot[sklearnex] installation option. · epistasislab.github.io, 9 Oct 2026
- License
- TPOT is free software distributed under the GNU Lesser General Public License version 3 or later, without warranty. · epistasislab.github.io, 9 Oct 2026
- Parallel processing
- TPOT uses Dask for parallel processing and recommends guarding script code with an if __name__ == "__main__" block. · epistasislab.github.io, 9 Oct 2026
- Pipeline types
- The documentation includes classifiers and regressors, as well as graph, sequential, tree, and union pipeline types. · epistasislab.github.io, 9 Oct 2026
- Platform caveat
- The documentation warns that scikit-learn extensions may have compatibility or performance issues on Arm-based CPUs such as M1 Macs. · epistasislab.github.io, 9 Oct 2026
- Preprocessing
- With preprocessing enabled, TPOT imputes missing values, one-hot encodes categorical features, and standardizes data. · epistasislab.github.io, 9 Oct 2026
- Preprocessing limit
- The documentation says preprocessing is currently fitted and transformed on the entire training set before cross-validation splitting. · epistasislab.github.io, 9 Oct 2026
- Purpose
- TPOT is a Python automated machine learning tool that optimizes machine learning pipelines using genetic programming. · epistasislab.github.io, 9 Oct 2026
- Research focus
- The project says TPOT was developed in the Artificial Intelligence Innovation Lab at Cedars-Sinai with NIH funding. · epistasislab.github.io, 9 Oct 2026
- Support
- The project directs users to its GitHub issues to report bugs or suggest enhancements and to discuss extensions. · epistasislab.github.io, 9 Oct 2026
Where TPOT stands on Freedom251
- Predictive Analytics Software#14 of 37
- AutoML Software#15 of 30