title: Support Vector Machines for Differential Prediction.
abstract: Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest indifferential prediction. In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive performance for some outcome between subgroups in a population. We discuss adapting maximum margin classifiers for differential prediction. We first introduce multiple approaches that do not affect the key properties of maximum margin classifiers, but which also do not directly attempt to optimize a standard measure of differential prediction. We next propose a model that directly optimizes a standard measure in this field, theupliftmeasure. We evaluate our models on real data from two medical applications and show excellent results.
authors.affiliation: University of Wisconsin - Madison, Madison, WI, USA.
authors.forename: Finn
authors.initials: F
authors.lastname: Kuusisto
authors.affiliation: University of Porto, Porto, Portugal.
authors.forename: Vitor
authors.initials: V
authors.lastname: Santos Costa
authors.affiliation: Amazon, Seattle, WA, USA.
authors.forename: Houssam
authors.initials: H
authors.lastname: Nassif
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Burnside
authors.forename: David
authors.initials: D
authors.lastname: Page
authors.forename: Jude
authors.initials: J
authors.lastname: Shavlik
keywords: support vector machine; uplift modeling