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Abstract:
When designing a two-alternative classifier, one ordinarily
aims to maximize the classifier's ability to discriminate between
members of the two classes. We describe a situation in a
real-world business application of machine-learning prediction in
which an additional constraint is placed on the nature of the
solution: that the classifier achieve a specified correct
acceptance or correct rejection rate (i.e., that it achieve a
fixed accuracy on members of one class or the other). Our domain
is predicting
churn
in the telecommunications industry. Churn refers to customers
who switch from one service provider to another. We propose four
algorithms for training a classifier subject to this domain
constraint, and present results showing that each algorithm
yields a reliable improvement in performance. Although the
improvement is modest in magnitude, it is nonetheless impressive
given the difficulty of the problem and the financial return that
it achieves to the service provider.
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