Part 2 H2O AutoML Deep Dive

This part will help you understand the inner workings of H2O AutoML. This will involve how H2O AutoML handles data processing, training, and the selection of models, and how it measures the performance of trained models. This part will also help you understand how to read the various performance graphs and other model details that will help make sense of the models’ behavior. All of this will help you further experiment and explore H2O AutoML and get the most out of it based on your needs.

This section comprises the following chapters:

• Chapter 3, Understanding Data Processing
• Chapter 4, Understanding H2O AutoML Training and Architecture
• Chapter 5, Understanding AutoML Algorithms
• Chapter 6, Understanding H2O AutoML Leaderboard and Other Performance Metrics
• Chapter 7, Working with Model Explainability