Introduction to Generalized Linear Models in H2O

This tutorial introduces H2O's Generalized Linear Models (GLM) framework in R.

Generalized Linear Models (GLM)

Intuition: A linear combination of predictors is sufficient for determining an outcome.

Important components:
  1. Exponential family for error distribution (Gaussian/Normal, Binomial, Poisson, Gamma, Tweedie, etc.)
  2. Link function, whose inverse is used to generate predictions
  3. (Elastic Net) Mixing parameter between the L1 and L2 penalties on the coefficient estimates.
  4. (Elastic Net) Shrinkage parameter for the mixed penalty in 3.

R Documentation

The h2o.glm function fits H2O's Generalized Linear Models from within R.

library(h2o)
args(h2o.glm)

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

help(h2o.glm)

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

example(h2o.glm)

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

demo(h2o.glm)