Select and customize architectures and hyperparameters using random forest
More complex models often can be manually customized to improve how effective they are. Through exercises and explanatory content, we explore how altering the architecture of more complex models can bring about more effective results.
Learning objectives
In this module, you will:
- Discover new model types: decision trees and random forests.
- Learn how model architecture can affect performance.
- Practice working with hyperparameters to improve training effectiveness.
Prerequisites
Familiarity with machine learning models