**TPOT (Tree-based Pipeline Optimization Tool)** is an open-source Python library for **automated machine learning (AutoML)**. It helps data scientists and developers automatically design, optimize, and deploy machine learning pipelines, reducing manual tuning and improving model performance. TPOT leverages **genetic programming** to explore thousands of possible pipelines, including feature preprocessing, model selection, and hyperparameter optimization.

Features of TPOT:

Automates pipeline creation, feature selection, and hyperparameter tuning.
Built on top of **scikit-learn** for compatibility with Python ML workflows.
Supports both classification and regression tasks.
Generates Python code for the optimized machine learning pipeline.
Can be integrated into scripts or Jupyter notebooks for interactive ML experiments.
Open-source and actively maintained, used in research, prototyping, and production applications.

To check if TPOT is installed in your Python environment:

 $ source tpot-env/bin/activate # activate your virtual environment $ python -c "import tpot; print(tpot.__version__)"

Disclaimer: TPOT is released under the MIT License and maintained by the open-source community. Users are responsible for correct usage in their projects. Always refer to the official TPOT documentation and GitHub repository for the most accurate and up-to-date information.

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TPOT

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TPOT

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TPOT
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