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Decision Making with Machine Learning and ROC Curves

Feng Kai, Hong Han, Tang Ke and Wang Jingyuan

Available at SSRN 3382962, 2019. Download

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. 

Empirical ROC curves and doctors’ performance conditional on the feature of "previous pregnancy outcomes"
Empirical ROC curves and doctors’ performance conditional on the feature of "previous pregnancy outcomes"

If you find our work is helpful for your research, please kindly consider citing our paper.

 

@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}

}