  The learning curve expresses the error rate of a predictive modeling
procedure as a function of the sample size of the training dataset. It
typically is a decreasing, convex function with a positive limiting value. An
estimate of the learning curve can be used to assess whether a modeling
procedure should be expected to become substantially more accurate if
additional training data become available. This article proposes a new
procedure for estimating learning curves using imputation. We focus on
classification, although the idea is applicable to other predictive modeling
settings. Simulation studies indicate that the learning curve can be estimated
with useful accuracy for a roughly four-fold increase in the size of the
training set relative to the available data, and that the proposed imputation
approach outperforms an alternative estimation approach based on parameterizing
the learning curve. We illustrate the method with an application that predicts
the risk of disease progression for people with chronic lymphocytic leukemia.
