  The purpose of class distribution estimation (also known as quantification)
is to determine the values of the prior class probabilities in a test dataset
without class label observations. A variety of methods to achieve this have
been proposed in the literature, most of them based on the assumption that the
distributions of the training and test data are related through prior
probability shift (also known as label shift). Among these methods, Friedman's
method has recently been found to perform relatively well both for binary and
multi-class quantification. We discuss the properties of Friedman's method and
another approach mentioned by Friedman (called DeBias method in the literature)
in the context of a general framework for designing linear equation systems for
class distribution estimation.
