Uplift Modeling with ROC: An SRL Case Study.
Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, JUDE W. SHAVLIK · 2013
Abstract. Uplift modeling is a classification method that determines the incremental impact of an action on a given population. Uplift mod-eling aims at maximizing the area under the uplift curve, which is the difference between the subject and control sets ’ area under the lift curve. Lift and uplift curves are seldom used outside of the marketing domain, whereas the related ROC curve is frequently used in multiple areas. Achieving a good uplift using an ROC-based model instead of lift may be more intuitive in several areas, and may help uplift modeling reach a wider audience. We alter SAYL, an uplift-modeling statistical relational learner, to use ROC instead of lift. We test our approach on a screening mammography dataset. SAYL-ROC outperforms SAYL on our data, though not signif-icantly, suggesting that ROC can be used for uplift modeling. On the other hand, SAYL-ROC returns larger models, reducing interpretability. 1