Decision trees are one of the most common approaches used in supervised machine learning. Building a decision tree allows you to model complex relationships between variables by mimicking if-then-else decision-making as a naturally occurring human behavior. In this course, instructor Frederick Nwanganga gives you an overview of how to collect, explore, and transform your data in preparation for building decision tree models in Python.
Discover the power of decision trees, what they are, how they are built, and how they quantify impurity within a partition. Get tips from Frederick on building, visualizing, pruning, and using a decision tree in Python including classification trees and regression trees. By the end of this course, you’ll be ready to start making your own models and applying them to different domains.
