TPOT

Uses evolutionary algorithms to automatically search thousands of pipeline configurations, often uncovering high-performing models that manual tuning might miss, as described in its key features.

Supports optimizing for multiple criteria like accuracy and model complexity simultaneously, allowing for balanced model selection without manual trade-offs.

Leverages Dask for efficient computation across multiple cores or clusters, speeding up the optimization process as highlighted in the features.

Generates actual Python code for the optimized pipeline, empowering users to understand, modify, and deploy it, which is central to its value proposition.

Modular architecture allows for easy customization of evolutionary algorithms and objective functions, making it adaptable to specific research needs.