Exploring Optional Parameters for H2O AutoML
As we explored in Chapter 2, Working with H2O Flow (H2O’s Web UI), when training models using H2O AutoML, we had plenty of parameters to select. All these parameters gave us the capability to control how H2O AutoML should train our models. This control helps us get the best possible use of AutoML based on our requirements. Most of the parameters we explored were pretty straightforward to understand. However, there were some parameters whose purpose and effects were slightly complex to be understood at the very start of this book.
In this chapter, we shall explore these parameters by learning about the Machine Learning (ML) concepts behind them, and then understand how we can use them in an AutoML ...