Hyperparameter Optimization in H2O: Grid Search, Random Search and the Future R-bloggers

'Til your good is better and your better is best." H2O now has random hyperparameter search with time- and metric-based early stopping. Bergstra and Bengio[1] write on p. 281: Compared with neural networks configured by a pure grid search, we find that random search over the same domain is able to find models that are as good or better within a small fraction of the computation time. Even smarter means of searching the hyperparameter space are in the pipeline, but for most use cases random search does as well. Nearly all model algorithms used in machine learning have a set of tuning "knobs" which affect how the learning algorithm fits the model to the data.