Webinar: Improve Your CLASSIFICATION with CART(r) and RandomForests(r), Mar 29
We discuss the advantages of tree based techniques, including automatic variable selection, variable interactions, nonlinear relationships, outliers, and missing values.
Date: Wed, Mar 29, 2017
Time: 10am PST, 1pm EST
Speaker: Charles Harrison, Marketing Statistician, Salford Systems
In this webinar we'll introduce you to two tree-based machine learning algorithms, CART® decision trees and RandomForests®. Both of these methods can be used for either regression or classification (i.e. Y = "Application Denied" or "Application Accepted") and we will focus on classification in this presentation. We will discuss the advantages of tree based techniques including their ability to automatically handle variable selection, variable interactions, nonlinear relationships, outliers, and missing values. We'll explore the CART algorithm, bootstrap sampling, and the Random Forest algorithm (all with animations) and compare their predictive performance using a real world dataset.