Exercise

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

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Start defining a grid of hyperparameters for deep learning with h2o: for learning rate use the values 0.001, 0.005 and 0.01. For an overview of all hyperparameters to use, go to the help for h2o.deeplearning.