Select and customize architectures and hyperparameters using random forest

Beginner
AI Engineer
Data Scientist
Student
Azure

More complex models often can be manually customized to improve how effective they're. 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