Evolution of Classification: from Logistic Regression and Decision Trees to Bagging/Boosting and Netlift Modeling. Case Study Examples drawn from Datasets in Direct Marketing & Biomedical Data Analysis (ADDED FEE)

• Activity Number: 214609 - Evolution of Classification: From Logistic Regression and Decision Trees to Bagging/Boosting and Netlift Modeling - Case Study Examples Drawn from Data Sets in Direct Marketing and Biomedical Data Analysis (ADDED FEE)
• Type: Professional Development
• Date/Time: Wednesday, August 2, 2017 : 1:00 PM to 2:45 PM
• Sponsor: ASA
• Author(s): Mikhail Golovnya* and Charles Harrison* and Dan Steinberg*
• Companies: Salford Systems and Salford Systems and Salford Systems

Abstract

Not so long ago, modelers would use traditional classification, data mining and decision tree techniques to identify a target population. We have come a long way in recent years. By incorporating modern approaches, including boosting, bagging and netlift, there has been a giant leap in this arena. This presentation will discuss recent improvements to conventional decision tree and logistic regression technology via two case study examples: one in Direct Marketing & the second drawn from Biomedical Data Analysis. Within the context of real-world examples, we will illustrate the evolution of classification by contrasting and comparing: Regularized Logistic Regression, CART, Random Forests, TreeNet Stochastic Gradient Boosting, and RuleLearner.