Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

The Costs and Potential Benefits of Introducing the “I Don’t Know” Answer in Binary Classification Settings

Version 1 : Received: 24 August 2021 / Approved: 27 August 2021 / Online: 27 August 2021 (13:05:02 CEST)

How to cite: Krstajic, D. The Costs and Potential Benefits of Introducing the “I Don’t Know” Answer in Binary Classification Settings. Preprints 2021, 2021080521 (doi: 10.20944/preprints202108.0521.v1). Krstajic, D. The Costs and Potential Benefits of Introducing the “I Don’t Know” Answer in Binary Classification Settings. Preprints 2021, 2021080521 (doi: 10.20944/preprints202108.0521.v1).

Abstract

We are of the opinion that during the design of a binary classifier one ought to consider adding an “I don’t know” answer. We provide the case for the introduction of this third category when a human needs to make a decision based on the answer from a binary classifier. We discuss the costs and potential benefits of its introduction. Colloquially, we have used the term “I don’t know”, but formally we refer to it as NotAvailable. A procedure to define NotAvailable predictions in binary classifiers, called all leave-one-out models (ALOOM), is presented as proof of the concept. Furthermore, we discuss the potential benefits of applying ALOOM in real life applications.

Keywords

non-applicability domain; binary classification; ignorance; decision-making

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