Grid search with h2o

Now that you successfully trained a Random Forest model with h2o, you can apply the same concepts to training all other algorithms, like Deep Learning. In this exercise, you are going to apply a grid search to tune a model.

Remember that gradient boosting models have the hyperparameter learn_rate whereas deep learning models have the rate hyperparameter.

The h2o library has already been loaded and initialized for you.