Today's post is very special. It's written in collaboration with Axel de Romblay the author of the MLBox Auto-ML package that has gained a lot of popularity these last years.
If you haven't heard about this library, go and check it out on github: It encompasses interesting features, it's gaining in maturity and is now under active development.
In this post, we'll show you how you can easily use it to train an automated machine learning pipeline for a classification problem. It'll start off by loading and cleaning the data, removing drift, launching a strong pipeline of accelerated optimization and generating predictions.