Decision making with CART

Abstract

The objective of this poster project is to explore Classification and Regression Tree models for making decisions, predictions, and classifications. We also want to learn about the main components of CART and explore their many other applications. We will also present the construction of a decision tree along. This will include both the mathematics and coding required to make one. We will also define some key elements such as Gini impurity, information gain, root nodes, decision nodes, and leaf nodes. We will look at the advantages and limitations of decision trees and how they can be used to accurately predict both continuous and categorical variables. Additionally, this research project will provide an overview of the Classification and Regression Tree methodology, focusing on its core principles, algorithms, and application in various domains. Finally, we want to present the practical applications of CART and demonstrate its versatility and effectiveness in the workforce.

Details

Session 1

9:30am – 11:00am

Grand Salon