The Math Behind Decision Trees: An Exploration of Decision Trees

Decision trees are an effective supervised learning technique for predicting the class or value of a target variable. Unlike other supervised learning methods theyre well-suited to classification and regression tasks. Use this course to learn how to work with decision trees and classification distinguishing between rule-based and ML-based approaches. As you progress through the course investigate how to work with entropy Gini impurity and information gain. Practice implementing both rule-based and ML-based decision trees and leveraging powerful Python visualization libraries to construct intuitive graphical representations of decision trees. Upon completion youll be able to create use and share rule-based and ML-based decision trees.