Performance of Classification Models from a User Perspective

David Martens, Jan Vanthienen, Wouter Verbeke, Bart Baesens

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a complete framework to assess the overall performance of classification models from a user perspective in terms of accuracy, comprehensibility, and justifiability. A review is provided of accuracy and comprehensibility measures, and a novel metric is introduced that allows one to measure the justifiability of classification models. Furthermore, taxonomy of domain constraints is introduced, and an overview of the existing approaches to impose constraints and include domain knowledge in data mining techniques is presented. Finally, justifiability metric is applied to a credit scoring and customer churn prediction case.
Original languageEnglish
Pages (from-to)782-793
JournalDecision Support Systems
Volume51
Issue number4
Early online date1 Feb 2011
DOIs
Publication statusPublished - Nov 2011

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