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Decision Making with Machine Learning and ROC Curves

K. Feng, H. Hong, K. Tang, and J. Wang

Available at SSRN 3382962, 2019, working paper.

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.

Decision Making with Machine Learning and ROC Curves
feng2019decision.pdf
Adobe Acrobat Document 7.1 MB
feng-slideDec2019brief
feng-slideDec2019brief.pdf
Adobe Acrobat Document 11.4 MB

@article{feng2019decision,

  title={Decision Making with Machine Learning and ROC Curves},

  author={Feng, Kai and Hong, Han and Tang, Ke and Wang, Jingyuan},

  journal={Available at SSRN 3382962},

  year={2019}

}