Meeting

First-time Attendees : $35 membership + $20 lunch

presentation slides

paper: Alacart: Poor Man's Classification Trees by Leonardo Auslander

Classification refers to identifying probable responders to a marketing campaign, fraudsters in business practices, disease in clinical analysis for instance. In the world of large observational data sets, they typically contain missing values and models must be searched.

Tree modeling has the double advantage of being a search technique and of automatically impute missing values.

In this presentation, we will compare the two approaches by way of an example and, if time permits, we will also describe further developments in the area of tree modeling, such as gradient boosting.

Breiman's et al 1984 monograph is the basis for CART, one of the most important Classification And Regression Tree methods. Other tree methods and corresponding software include C4.5 (Quinlan, 1993), Mathsoft's S+ (Clark and Pregibon, 1992), Chaid (Kass). See Steinberg (1993) and references therein.

Leonardo Auslender is a statistician (and economist) with more than 25 years of business experience and SAS expertise. His area of expertise is in the area of Giga-Data Analysis and Methods, and has written papers and given lectures on Variable Selection, Missing Value Imputation, Tree Regression, Support Vector Machines, Market-Basket Analysis, Data Base Marketing, CRM, GDP and (Relative Price) Inflation studies, Expectation Formations, Productivity and Technology effects in the economy. He was a lecturer of Finance and Macroeconomics at Rutgers University.

He presented two seminars on Market Basket Analysis in New York City (Informs and Amcis), a two-day seminar at the NYC Direct Marketing Association on Variable and Feature Selection in November, 2004, on Colinearity and Variable Selection at the December 2005 SCMA meeting in Auburn, Alabama, on Modeling issues at the SAS M2007 and M2008 Data Mining Conferences and at the Informs in NYC.