Introduction to Random Forests in H2O

This tutorial introduces H2O's Random Forest framework in R.

Random Forests

Intuition: Average an ensemble of weakly predicting (larger) trees where each tree is de-correlated from all other trees.

Important components:

1. Number of trees
2. Maximum depth of tree
3. Number of variables randomly sampled as candidates for splits
4. Sampling rate for constructing data set to use on each tree

R Documentation

The h2o.randomForest function fits H2O's Random Forest from within R.

library(h2o)
args(h2o.randomForest)

The R documentation (man page) for H2O's Random Forest can be opened from within R using the help or ? functions:

help(h2o.randomForest)

We can run the example from the man page using the example function:

example(h2o.randomForest)

And run a longer demonstration from the h2o package using the demo function:

demo(h2o.randomForest)