IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-06649-8_3.html
   My bibliography  Save this book chapter

Conformal Prediction: Classification and General Case

In: Algorithmic Learning in a Random World

Author

Listed:
  • Vladimir Vovk

    (University of London, Royal Holloway)

  • Alexander Gammerman

    (University of London, Royal Holloway)

  • Glenn Shafer

    (Rutgers University)

Abstract

In this chapter we mainly concentrate on classification, where the label space Y is finite (and equipped with the discrete σ-algebra), after discussing regression in the previous one. Our first topic is criteria of efficiency of conformal predictors (Sect. 3.1); they will be applied in the next chapter (Sect. 4.3.8 ) to designing new conformal predictors. We give two more examples of nonconformity measures, specific to the case of classification, and illustrate one of the criteria on one of those measures (Sect. 3.2). Finally, we consider the case of Weak teacher Teacher weak “weak teachers”, which are allowed to provide the true label with a delay or not to provide it at all, in Sect. 3.3.

Suggested Citation

  • Vladimir Vovk & Alexander Gammerman & Glenn Shafer, 2022. "Conformal Prediction: Classification and General Case," Springer Books, in: Algorithmic Learning in a Random World, edition 2, chapter 0, pages 71-106, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-06649-8_3
    DOI: 10.1007/978-3-031-06649-8_3
    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:978-3-031-06649-8_3. 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.