Are you thirsty for predictions? – h2o

H2O is an open source machine learning / prediction platform from Code is hosted on github, here. APIs are available for R, Scala, Python, Java. A REST API is available for web services or applications.


R H20 Tutorial

Installing h2o package.

Creating a simple gradient boost model.

Node(s) store h2o data frames. The R workspace variables maintain references to h2o data frames. Removing an h2o object from the R workspace will not delete it from the cluster.  In the example above, destination_frames, defines h2o data frame names on the cluster. Logging into a running cluster or node can be done, by ip address : port. In the above example localhost:54321 should work. With the web interface this tutorial could have been done with no R at all.

I look forward to exploring functionality within h2o especially deep learning models. I will post later how to deploy an h2o cluster in the near future.