Week 5 - Decision Trees

Learning Objectives

After today's class, you should be able to:

• Understand the decision tree algorithm for classification tasks, enabling the extraction of interpretable rules to inform effective decision making.
• Develop and implement decision tree classification models and visualize the output tree structure in Python.
• Apply the steps of the decision tree algorithm manually to identify the best attribute for splitting the data and use these results to construct the decision tree.

Class Agenda

• Classification Decision Tree Slides
• Decision Trees Python Tutorial
• In Class Time for HW2_Decision_Trees

Task List

• Required Reading: Chapter 9.1-9.2 - Classification Trees
• Complete and Submit HW2_Decision_Trees on Canvas.

Additional Resources

• Chapter 9 Code Examples
• HW2_Decision_Trees_Worksheet