TPOT (Tree-based Pipeline Optimization Tool) is a Python Automated Machine Learning software (AutoML) that optimizes machine learning pipelines using genetic programming. TPOT automates even the most tedious parts of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. It’s built on top of scikit-learn.
TPOT is still under active development so make sure to check back on its repository regularly for updates.
- Can be used on the command line or with Python code.
- You can run multiple instances of TPOT in parallel for a long ime (hours to day) to allow TPOT to thoroughly search the pipeline space for your dataset.
- Optimizes pipelines for regression problems.
- Comes with a handful of default operators and parameter configurations that help to optimize machine learning pipelines.
- Custom configuration for your operators and parameters
- Templates that help to reduce TPOT computation time and potentially provide more interpretable results.
- FeatureSetSelector – an operator that enables feature selection based on previous expert knowledge
- Pipeline caching
- Support for neural network models and deep learning
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