Tree-Based Pipeline Optimization Tool (TPOT) is using genetic programming to find the best performing ML pipelines, and it is built on top of scikit-learn.

Once your dataset is cleaned and ready to be used, TPOT will help you with the following steps of your ML pipeline:

  • Feature preprocessing
  • Feature construction and selection
  • Model selection
  • Hyperparameter optimization

Once TPOT is done with its experimentation, it will provide you with the best performing pipeline.

TPOT is very user-friendly as it's similar to using scikit-learn's API:

from tpot import TPOTClassifierfrom sklearn.datasets import load_digitsfrom sklearn.model_selection import train_test_split# Digits dataset that you have used in Auto-sklearn exampledigits = load_digits() ...

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