IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-031-16990-8_14.html
   My bibliography  Save this book chapter

Voting and Bagging

In: Machine Learning for Practical Decision Making

Author

Listed:
  • Christo El Morr

    (York University)

  • Manar Jammal

    (York University)

  • Hossam Ali-Hassan

    (York University, Glendon Campus)

  • Walid El-Hallak

    (Ontario Health)

Abstract

The ensemble technique relies on the idea that aggregation of many classifiers and regressors will lead to a better prediction [1]. In this chapter, we will introduce the ensemble technique and cover two ways in which to organize an ensemble (literally, a set) of machine learning methods called voting and bagging [2] and one algorithm to perform bagging called random forest [1, 3]. The other two ways to organize the ensemble methods are called boosting and stacking, which will be covered in the next chapter.

Suggested Citation

  • Christo El Morr & Manar Jammal & Hossam Ali-Hassan & Walid El-Hallak, 2022. "Voting and Bagging," International Series in Operations Research & Management Science, in: Machine Learning for Practical Decision Making, chapter 0, pages 413-430, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-16990-8_14
    DOI: 10.1007/978-3-031-16990-8_14
    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 search 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:isochp:978-3-031-16990-8_14. 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.