Practical Machine Learning with H2O

Chapter 10. Everything Else

There is lots still to say: enough to fill another book! This chapter will introduce quite a few topics, without going into too much detail, but pointing you toward where you can find out more.

This chapter starts with a look at where to get the latest documentation, and how to Use The Source, Luke! Then we look at how to upgrade H2O, and how to install it from source. Which leads on to how to set up clusters, which leads on to Spark and Sparkling Water. Finally, a look at other algorithms: naive bayes and ensembles.

Staying on Top of and Poking into Things

H2O is well-documented, so http://docs.h2o.ai should be your first port of call. The latest user guide is at http://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html.

If you want to see if, say, any new parameters have been added to GBM, you could go to the REST API Reference, then find GBMParametersV3. Alternatively, regularly check Changes.md over on the GitHub site!

If you wanted to see how GBM is implemented in Java you would start at the Javadocs, find "hex.tree.gbm,” then "GBM.” The corresponding code will be over on Github.

Random forest is called DRF in the REST endpoints, and hex.tree.drf in the Java source; everything else is fairly much named how you would expect.

Installing the Latest Version

Installing from packages, as shown back in the first chapter (“Install H2O with R (CRAN)” and “Install H2O with Python (pip)”) is going to be good enough for most people. However, installing the ...