Currently the metalearner is hardcoded as a default H2O GLM with non-negative weights. (`non_negative = TRUE`)
We need to add a `metalearner_algorithm` argument to allow customization of the metalearning algorithm.
Initially, this argument will take in either "glm" (default), "gbm", "drf", or "deeplearning".
A user cannot specify the metalearner model hyperparameters at this point. This will be completed with the addition of another argument, e.g. `metalearner_params`
junit, runit, pyunit for this argument is included