Introduction to Gradient Boosting Machines in H2O

This tutorial introduces H2O's Gradient (Tree) Boosting Machines framework in R.

Gradient Boosting Machines (GBM)

Intuition: Average an ensemble of weakly predicting (small) trees where each tree "adjusts" to the "mistakes" of the preceding trees.
Important components:
  1. Number of trees
  2. Maximum depth of tree
  3. Learning rate ( shrinkage parameter)

where smaller learning rates tend to require larger number of tree and vice versa.

R Documentation

The h2o.gbm function fits H2O's Gradient Boosting Machines from within R.

library(h2o)
args(h2o.gbm)

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

help(h2o.gbm)

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

example(h2o.gbm)

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

demo(h2o.gbm)