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Using Predicted Outcome Stratified Sampling to Reduce the Variability in Predictive Performance of a One-Shot Train-and-Test Split for Individual Customer Predictions

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  • G. VERSTRAETEN
  • D. VAN DEN POEL

    ()

Abstract

Since it is generally recognized that models evaluated on the data that was used for constructing them are overly optimistic, in predictive modeling practice, the assessment of a model’s predictive performance frequently relies on a one-shot train-and-test split between observations used for estimating a model, and those used for validating it. Previous research has indicated the usefulness of stratified sampling for reducing the variation in predictive performance in a linear regression application. In this paper, we validate the previous findings on six real-life European predictive modeling applications for marketing and credit scoring using a dichotomous outcome variable. We find confirmation for the reduction in variability using a procedure we describe as predicted outcome stratified sampling in a logistic regression model, and we find that the gain in variation reduction is – also in large data sets – almost always significant, and in certain applications markedly high.

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File URL: http://www.feb.ugent.be/nl/Ondz/wp/Papers/wp_06_360.pdf
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Bibliographic Info

Paper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 06/360.

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Length: 10 pages
Date of creation: Jan 2006
Date of revision:
Handle: RePEc:rug:rugwps:06/360

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  1. Young-Hoon Park & Peter S. Fader, 2004. "Modeling Browsing Behavior at Multiple Websites," Marketing Science, INFORMS, vol. 23(3), pages 280-303, May.
  2. Joffre Swait & Rick L. Andrews, 2003. "Enriching Scanner Panel Models with Choice Experiments," Marketing Science, INFORMS, vol. 22(4), pages 442-460, September.
  3. Dasgupta, Chanda Ghose & Dispensa, Gary S. & Ghose, Sanjoy, 1994. "Comparing the predictive performance of a neural network model with some traditional market response models," International Journal of Forecasting, Elsevier, vol. 10(2), pages 235-244, September.
  4. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
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Cited by:
  1. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, Ghent University, Faculty of Economics and Business Administration.

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