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Automated ML pipelines powered by genetic programming.
TPOT automatically designs and optimizes scikit-learn pipelines using evolutionary algorithms, offering feature selection, multi-objective search, and modular customization for faster model development.
TPOT (Tree‑based Pipeline Optimization Tool) is a Python library that automatically constructs and tunes scikit‑learn pipelines using genetic programming. Designed for data scientists, ML engineers, and researchers, it removes much of the manual trial‑and‑error involved in model selection and preprocessing.
The rewritten TPOT2 core introduces graph‑based pipeline representation, genetic feature selection, flexible search‑space definitions, and multi‑objective optimization that balances accuracy with model complexity. Its modular architecture lets users replace mutation, crossover, or selection strategies, while Dask integration provides parallel evaluation on local cores or clusters. Installation works with Python 3.10–3.13 and a standard scientific stack; optional sklearnex extensions accelerate certain estimators, though they may need extra care on ARM CPUs. TPOT can be run from notebooks or scripts (protecting entry‑point code with if __name__ == "__main__"). The library is well‑documented, includes tutorial notebooks, and welcomes contributions via its GitHub repository.
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Rapid baseline generation for new datasets
TPOT discovers a performant scikit‑learn pipeline in minutes, providing a strong starting point for further refinement.
Feature selection in high‑dimensional biomedical data
Genetic feature selection isolates predictive biomarkers while optimizing model accuracy.
Multi‑objective model search balancing accuracy and complexity
Produces compact pipelines that meet performance targets and are easier to interpret.
Custom evolutionary strategies for research
Researchers plug in bespoke mutation operators to explore novel pipeline structures.
TPOT requires Python ≥3.10 and <3.14.
It uses Dask to distribute the evaluation of candidate pipelines across multiple processes or a cluster.
Yes, the modular framework allows users to add or replace mutation, crossover, and selection components.
TPOT can incorporate GPU‑enabled libraries like XGBoost and LightGBM, but extra sklearnex extensions may have limited ARM compatibility.
Install LightGBM from conda‑forge first (`conda install -c conda-forge 'lightgbm>=3.3.3'`) then install TPOT.
Project at a glance
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