Summary: Monotonicity Hints for Credit Screening
Joseph Silly, Yaser AbuMostafaz
y Computation and Neural Systems program, California Institute of Technology
z Departments of Electrical Engineering and Computer Science, California Institute of Technology
Abstract--- A hint is any piece of side information about the target function to be learned.
We describe an application of monotonicity hints to a real world problem. The task con
sidered is the screening of credit card applicants. A measure of the monotonicity error of a
candidate function is defined and an objective function for the enforcement of monotonicity
is derived from Bayesian principles. We report experimental results which show that using
monotonicity hints leads to a statistically significant improvement in performance on the
credit screening problem.
Designing a machine learning model involves balancing two conflicting concerns . The model used
should be powerful enough to implement the details of the unknown target function f , but simple enough
that the optimal parameters for doing so may be estimated accurately from the available data.
The use of hints  in the learning process allows us to satisfy the second requirement better without
compromising the first. A hint is any piece of information known about f beyond the available input
output examples. For instance, f may be known to be invariant or symmetric with respect to some