Uplift Modeling with ROC: An SRL Case Study


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 significantly , suggesting that ROC can be used for uplift modeling. On the other hand, SAYL-ROC returns larger models, reducing interpretability.

Extracted Key Phrases

2 Figures and Tables

Showing 1-10 of 11 references

Data Mining and Statistics for Decision Making

  • S Tufféry
  • 2011

An integrated approach to learning Bayesian Networks of rules

  • J Davis, E S Burnside, I De Castro Dutra, D Page, Santos Costa
  • 2005

Incremental value modeling

  • B Hansotia, B Rukstales
  • 2002

Differential response analysis: Modeling true response by isolating the effect of a single action

  • N J Radcliffe, P D Surry
  • 1999