Comments on Friedman's Method for Class Distribution Estimation

TL;DR

This paper analyzes Friedman's method for estimating class distributions in test data under prior probability shift, discussing its properties and comparing it with the DeBias approach within a general linear system framework.

Contribution

It provides a detailed analysis of Friedman's method and the DeBias approach, clarifying their properties within a unified linear equation system framework.

Findings

Friedman's method performs well for binary and multi-class quantification.

The paper clarifies the theoretical properties of Friedman's and DeBias methods.

It situates these methods within a general framework for class distribution estimation.

Abstract

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.