IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-319-89824-7_77.html

Could Machine Learning Predict the Conversion in Motor Business?

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Lorenzo Invernizzi

    (Zurich Insurance Company Ltd)

  • Vittorio Magatti

    (Sapienza Universita’ di Roma)

Abstract

The aim of the paper is to estimate the Conversion Rate by means of three Machine Learning (ML) algorithms: Classification and Regression Tree (CART), Random Forest (RF) and Gradient Boosted Tree (BOOST). The Generalized Linear Model (GLM), benchmark model in the framework, is used as frame of reference. The RF model has the highest Recall, while the BOOST is the most precise model. The RF is able to outperform the GLM benchmark model in terms of Log Loss error, Precision, Recall and F Score. Variable Importance and Strength index, computed from the ML models and the GLM respectively, describe how the different algorithms are coherent on choosing the most relevant features.

Suggested Citation

  • Lorenzo Invernizzi & Vittorio Magatti, 2018. "Could Machine Learning Predict the Conversion in Motor Business?," Springer Books, in: Marco Corazza & María Durbán & Aurea Grané & Cira Perna & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 431-435, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-89824-7_77
    DOI: 10.1007/978-3-319-89824-7_77
    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

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:978-3-319-89824-7_77. 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.