[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/rce/rvceco/19971.html
   My bibliography  Save this article

A methodology to find the best classifier in business decision

Author

Listed:
  • Vega Vilca, José C.
  • Torres Núñez, David A.
Abstract
In this research, a methodology is presented to improve strategies of analysis in situations where supervised classification becomes the fundamental tool for business decision. The need to categorize the new customers into one of several groups, according to the characteristics of the subject, is analyzed through the calculation of the error rate. Programs were written using the statistical software package R, to calculate the error rate of each of nine classifiers, using cross-validation method 10 (Stone, 1974), in the 50 permutations of the data under consideration. For each of the analyzed data sets it was demonstrated, through ANOVA, that there are indeed significant differences in the average error rates of classifiers (p=0.00); therefore, it is concluded that the best classifier is the one with the lowest error rate.

Suggested Citation

  • Vega Vilca, José C. & Torres Núñez, David A., 2015. "A methodology to find the best classifier in business decision," Revista de Ciencias Económicas, Instituto de Investigaciones en Ciencias Económicas, Universidad de Costa Rica, vol. 33(1), January.
    • Handle: RePEc:rce:rvceco:19971
    as

    Download full text from publisher

    File URL: https://revistas.ucr.ac.cr/index.php/economicas/article/view/19971
    Download Restriction: no
    ---><---

    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:rce:rvceco:19971. 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: Luis J. Hall (email available below). General contact details of provider: https://edirc.repec.org/data/iiucrcr.html .

    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.