Hands-On Deep Learning with R

An H2O example

For this example, we will again use the adult census dataset to predict income. As with our Keras example, this will be kept extremely minimal and we will cover just enough to illustrate the syntax for working with H2O, as well as the design nuances that differ from other packages:

1. The first major difference when working with H2O is that we must explicitly initialize our H2O session, which will generate a Java Virtual Machine instance and connect it with R. This is accomplished with the following lines of code:

# load H2O packagelibrary(h2o)# start H2Oh2o::h2o.init()

2. Loading data to use with H2O requires converting the data to H2OFrame. H2OFrame is very similar to data frames, with the major distinction having to do with ...