Decision Making with Machine Learning and ROC Curves

52 Pages Posted: 30 May 2019

See all articles by Kai Feng

Kai Feng

Beihang University (BUAA)

Han Hong

Stanford University

Ke Tang

Institute of Economics, School of Social Sciences, Tsinghua University

Jingyuan Wang

Beihang University (BUAA)

Date Written: May 5, 2019

Abstract

The Receiver Operating Characteristic (ROC) curve is a representation of the statistical information discovered in binary classification problems and is a key concept in machine learning and data science. This paper studies the statistical properties of ROC curves and its implication on model selection. We analyze the implications of different models of incentive heterogeneity and information asymmetry on the relation between human decisions and the ROC curves. Our theoretical discussion is illustrated in the context of a large data set of pregnancy outcomes and doctor diagnosis from the Pre-Pregnancy Checkups of reproductive age couples in Henan Province provided by the Chinese Ministry of Health.

Keywords: ROC Curve, Binary Classification, Neyman Pearson Lemma, Incentive Heterogeneity, Information Asymmetry

JEL Classification: C44, C45, D81

Suggested Citation

Feng, Kai and Hong, Han and Tang, Ke and Wang, Jingyuan, Decision Making with Machine Learning and ROC Curves (May 5, 2019). Available at SSRN: https://ssrn.com/abstract=3382962 or http://dx.doi.org/10.2139/ssrn.3382962

Kai Feng

Beihang University (BUAA) ( email )

37 Xue Yuan Road
Beijing 100083
China

Han Hong

Stanford University ( email )

Landau Economics Building
579 Serra Mall
Stanford, CA 94305-6072
United States

Ke Tang (Contact Author)

Institute of Economics, School of Social Sciences, Tsinghua University ( email )

No.1 Tsinghua Garden
Beijing, 100084
China
13466777332 (Phone)

Jingyuan Wang

Beihang University (BUAA) ( email )

37 Xue Yuan Road
Beijing 100083
China

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