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() ...
