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.
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Notes
- 1.
Strictly speaking, only one less variable is required because its value can be derived from the knowledge of all other dummy variables.
- 2.
Since for different numbers of levels between variables, the \(\chi ^2_{\mathrm {net},2}\) statistic has different approximate distributions and it would be incorrect to sort with respect to \(\chi ^2_{\mathrm {net},2}\).
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Michel, R., Schnakenburg, I., von Martens, T. (2019). Supplementary Methods for Variable Transformation and Selection. In: Targeting Uplift. Springer, Cham. https://doi.org/10.1007/978-3-030-22625-1_5
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DOI: https://doi.org/10.1007/978-3-030-22625-1_5
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