Supplementary Methods for Variable Transformation and Selection

René Michel, Igor Schnakenburg and Tobias von Martens
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René Michel: Deutsche Bank AG
Igor Schnakenburg: DeTeCon International GmbH
Tobias von Martens: Deutsche Bank AG

Chapter Chapter 5 in Targeting Uplift, 2019, pp 121-136 from Springer

Abstract: Abstract In net scoring as well as in gross scoring, the analyst has an impact on model development not only by choosing the modeling method. As part of data preparation or modeling itself, the analyst tries to adjust the available data in order to improve model results and/or stability and/or more discrimination power. In this chapter a closer look will be taken at two important methods for those adjustments. The first method deals with the possible transformation of raw data in order to improve performance of net scoring methods. The second method explains the so-called variable preselection, i.e., the process of reducing all available variables to a suitable subset which enter model building.

Date: 2019
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DOI: 10.1007/978-3-030-22625-1_5

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