4 Python AutoML Libraries Every Data Scientist Should Know

With the use of recent methods like Bayesian Optimization, the library is built to navigate the space of possible models and learns to infer if a specific configuration will work well on a given task. Created by Matthias Feurer, et al., the library's technical details are described in a paper, Efficient and Robust Machine Learning. In addition to discovering data preparation and model selections for a dataset, it learns from models that perform well on similar datasets. Top-performing models are aggregated in an ensemble. On top of an efficient implementation, auto-sklearn requires minimal user interaction.