Understanding how H2O AutoML performs hyperparameter optimization and training

Throughout the course of this book, we have marveled at how the AutoML process automates the sophisticated task of training and selecting the best model without us needing to lift a finger. Behind every automation, however, there is a series of simple steps that is executed in a sequential manner.

Now that we have a good understanding of H2O’s architecture and how to use H2O AutoML to train models, we are now ready to finally open the black box, that is, H2O AutoML. In this section, we shall understand what H2O AutoML does behind the scenes so that it automates the entire process of training and selecting the best ML models.

The answer to this question is pretty simple. H2O AutoML automates the entire ML process using grid search hyperparameter optimization.

Grid search hyperparameter optimization sounds very intimidating to a lot of non-experts, but the concept in itself is actually very...