Evaluating discrete choice prediction models when the evaluation data is corrupted: analytic results and bias corrections for the area under the ROC

DOI10.1007/s10618-015-0437-7zbMath1416.62540OpenAlexW2140018002MaRDI QIDQ1741248

Roger M. Stein

Publication date: 3 May 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/1721.1/106979

zbMATH Keywords

predictionmachine learningmodel validationbias correctionROCmisclassificationdata corruptioncredit models

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Nonparametric hypothesis testing (62G10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• HandTill2001

Cites Work

• Ignorability and coarse data
• The area above the ordinal dominance graph and the area below the receiver operating characteristic graph
• Misclassification of the dependent variable in a discrete-response setting
• Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach
• A Class of Statistics with Asymptotically Normal Distribution
• Robust classification for imprecise environments
• A simple generalisation of the area under the ROC curve for multiple class classification problems

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