h2oai/h2o-3

In this tutorial, we show how to build a well-tuned H2O GBM model for a supervised classification task. We specifically don't focus on feature engineering and use a small dataset to allow you to reproduce these results in a few minutes on a laptop. This script can be directly transferred to datasets that are hundreds of GBs large and H2O clusters with dozens of compute nodes. This tutorial is written in R Markdown. Either download H2O from H2O.ai's website or install the latest version of H2O into R with the following R code: Everything is scalable and distributed from now on.