Working with model training functions in H2O Flow

Once your dataset is ready, the next step of the model training pipeline is the actual training part. The training of models can get very complex as there are a lot of configurations that decide how the model will be trained on the dataset. This is true even for AutoML where the majority of the hyperparameter tuning is done behind the scenes. Not only are there right and wrong ways of training a model for a specific type of data, but some of the configuration values can also affect the performance of the model. Therefore, it is important to understand the various configuration parameters that H2O has to offer when training a model using AutoML. In this section, we will focus on understanding what these parameters are and what they do when it comes to model training.

We will understand how to train a model using AutoML, step by step, using the dataframes we created previously.

Note that there are plenty of things in this section...