On the recordRead 9 Oct 202614 facts
TPOT
Plans and prices
TPOT
Free
Free software under LGPL-3.0-or-later
Read 9 Oct 2026 · epistasislab.github.io
On the record
- 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 · read 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 · read 9 Oct 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 · read 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 · read 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 · read 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 · read 9 Oct 2026
- Pipeline types
- The documentation includes classifiers and regressors, as well as graph, sequential, tree, and union pipeline types.epistasislab.github.io · read 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 · read 9 Oct 2026
- Preprocessing
- With preprocessing enabled, TPOT imputes missing values, one-hot encodes categorical features, and standardizes data.epistasislab.github.io · read 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 · read 9 Oct 2026
- Purpose
- TPOT is a Python automated machine learning tool that optimizes machine learning pipelines using genetic programming.epistasislab.github.io · read 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 · read 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 · read 9 Oct 2026
