The problem of modeling binary responses by using cross-sectional data has been addressed
with a number of satisfying solutions that draw on both parametric and nonparametric
methods. However, there exist many real situations where one of the two responses (usually
the most interesting for the analysis) is rare. It has been largely reported that this class
imbalance heavily compromises the process of learning, because the model tends to focus on
the prevalent class and to ignore the rare events. However, not only the estimation of the
classification model is affected by a skewed distribution of the classes, but also the evaluation
of its accuracy is jeopardized, because the scarcity of data leads to poor estimates of the
model’s accuracy.
In this work, the effects of class imbalance on model training and model assessing are
discussed. Moreover, a unified and systematic framework for dealing with both the problems is proposed, based on a smoothed bootstrap re-sampling technique.
