Automated machine learning, also known as AutoML, is an emerging field in which the process of building machine learning models to model data is automated. AutoML can make modelling easier and more accessible for everyone. In this article, I’ll walk you through which Python AutoML libraries you should start practising to stay ahead of the competition.
Python AutoML Libraries You Should Know
Here are the four Python AutoML Libraries that every machine Learning practitioner should know:
auto-sklearn is an automated machine learning toolkit that integrates seamlessly with the standard sklearn interface that many in the community are familiar with. With the use of recent methods like Bayesian optimization, the library is designed to navigate the space of possible models and learn to deduce whether a specific configuration will perform well on a given task.
Like we start Machine Learning by working on the algorithms provided by Scikit-Learn, likewise starting auto-sklearn for AutoML from all the python AutoML libraries is not a bad choice. In addition to learning about data preparation and model selections for a dataset, it learns from models that perform well on similar datasets. The best performing models are grouped into a set.
TPOT is also one of the AutoML Python libraries that automates the modelling pipeline, with a greater emphasis on data preparation as well as modelling algorithms and model hyperparameters. It automates the selection, preprocessing and construction of functionalities thanks to a scalable tree structure.
TPOT pipeline optimizers can take a few hours to produce great results, as do many AutoML algorithms unless the dataset is small. You can easily run these long programs in Kaggle commits or Google Colab.
HyperOpt is also one of the best Python AutoML libraries for Bayesian optimization, developed by James Bergstra. It is designed for the task of optimizing models with hundreds of parameters, HyperOpt is explicitly used to optimize machine learning pipelines, with additional options to scale the process across multiple cores and machines.
However, HyperOpt is difficult to use directly, as it is very technical and requires carefully specified optimization procedures and parameters. Instead, it is recommended to use HyperOpt-sklearn, a wrapper around HyperOpt that integrates the sklearn library.
Neural networks and deep learning are significantly more powerful, and therefore more difficult to automate than standard machine learning libraries.
Using AutoKeras, you can create a model with complex elements such as spatial incorporations and reductions that would otherwise be less accessible to those who are still learning deep learning.
When AutoKeras creates models for you, much of preprocessing, like vectorizing or cleaning text data, is done and optimized for you. It takes two lines to initiate and train a search. AutoKeras boasts a Keras-like interface, so it’s not hard to remember and use at all.
So these were the four Python AutoML libraries that you should learn to stay ahead of the competition. I hope you liked this article on the most important Python AutoML Libraries. Feel free to ask your valuable questions in the comments section below.