**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.