‘Lazy Predict’ Tutorial for AutoML with scikit-learn [Eryk Lewinson]

Eryk Lewinson wrote a tutorial on how to use Lazy Predict, which is an automated machine learning (AutoML) extension by Shankar Rao Pandala for scikit-learn. Automatisation is also becoming more prominent for recommender-systems (AutoRecSys) and hence this library could be useful for recommender-systems developers.

While starting to work on a supervised learning problem, we are often not sure which models will work well with the dataset at hand. A possible solution would be to start with a model that works fairly well for many datasets (like Random Forest) and then iterate. This way, we can establish a benchmark we aim to improve.

Most likely, at some point in time, you also considered throwing ALL the models at the dataset and just seeing what will happen. However, this was not that simple to code (or rather, it was quite tedious), so probably you gave up on the idea and just tried a few models that you have worked with, in the past. But now, there is lazypredict, with which you can train all the models available in scikit-learn (and more, like XGBoost and LightGBM) with a single line of code. Sounds great, right? Let’s see how this works out in practice!

https://towardsdatascience.com/lazy-predict-fit-and-evaluate-all-the-models-from-scikit-learn-with-a-single-line-of-code-7fe510c7281