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Gradient Boosting and Comparative Performance in Business Applications


Business applications usually involve classification issues, such as response/no-response in direct marketing, customer attrition and fraud detection in many industries, etc. Many techniques are used, such as logistic regression, neural networks and trees. We will present Friedman’s Gradient Boosting (GB) algorithm that belongs in the data mining toolbox for both classification and regression problems. We will start by focusing initially on the issue of the bias-variance trade, and then present a simple GB algorithm.
Also presented will be an application in the classification context of a direct marketing application and compare the relative performance of GB to other methods, such as logistic regression and classification trees. We will provide Linear dependence plots to compare the relative model interpretation of these different methods; and conclude with final remarks on pros and cons of the method.

Take aways:

  • Ensemble models in direct marketing and their usefulness
  • Comparison of models by predictors and performance
  • Visualization (partial dependency plots and visualization of Random Forests and Gradient Boosting results)
  • Tuning Gradient Boosting.



Leonardo Auslender

Independent Research Statistician

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, at the Informs in NYC and at NJSUG.

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