Yuri Verges (IME-USP)

Title: Graphical Machine Learning Methods

The area under the ROC curve (AUC) is a popular metric for the class-distribution robust learning framework. However, the traditional machine learning models trained with AUC are not well studied for cost sensitive decision problems.

The aim in this talk is to apply the methodology suggested by Shao et al. (2023) [Weighted ROC Curve in Cost Space: Extending AUC to Cost-Sensitive Learning. Advances of Neural Information Processing Systems (NeurIPS 2023) 36, 17257-17368] to get the corresponding weighted AUC version in cost space, enriching the existing graphical tools and their interpretations. Related minimization of the mean cost functional will be discussed as well. The proposed tools will be illustrated in empirical economics examples (joint work with Nikolai Kolev).