IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-22625-1_2.html
   My bibliography  Save this book chapter

The Traditional Approach: Gross Scoring

In: Targeting Uplift

Author

Listed:
  • René Michel

    (Deutsche Bank AG)

  • Igor Schnakenburg

    (DeTeCon International GmbH)

  • Tobias von Martens

    (Deutsche Bank AG)

Abstract

Model building and scoring as a statistical methodology have been known for decades, and there is a wide variety of literature available for studies. Instead of giving a complete introduction into model building and scoring techniques, it is the intention of this chapter to explain the main predictive modeling techniques from an angle which allows the reader to understand the change in paradigm that comes with the transition from classical scores to net scores. At first, the problem to be solved is explained and formalized. The second section introduces common methods for scoring, like decision trees or (logistic) regression, always with the generalization to net scoring in mind. The third section contains an introduction to well-known quality measures for scoring models. Although the facts presented in this chapter may be known to many readers, it is nevertheless recommended to study this chapter in order to get familiar with the way scoring methods are presented and described in this book.

Suggested Citation

  • René Michel & Igor Schnakenburg & Tobias von Martens, 2019. "The Traditional Approach: Gross Scoring," Springer Books, in: Targeting Uplift, chapter 0, pages 7-43, Springer.
  • Handle: RePEc:spr:sprchp:978-3-030-22625-1_2
    DOI: 10.1007/978-3-030-22625-1_2
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-030-22625-1_2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.