# Comments on Friedman's Method for Class Distribution Estimation

arXiv: https://arxiv.org/abs/2405.16666 · HTML: https://mlsanity.com/paper/2405.16666/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 26 May 2024.

## Builds on (most cited first, 7 of 7)

- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [Quantification under prior probability shift: the ratio estimator and its extensions](https://mlsanity.com/paper/1807.03929/) (2018)
- [A Comparative Evaluation of Quantification Methods](https://mlsanity.com/paper/2103.03223/) (2021)
- [ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models](https://mlsanity.com/paper/2305.19123/) (2023)
- [Unified Framework for Quantification](https://mlsanity.com/paper/1606.00868/) (2016)
- [Minimising quantifier variance under prior probability shift](https://mlsanity.com/paper/2107.08209/) (2021)
- [Exact fit of simple finite mixture models](https://mlsanity.com/paper/1406.6038/) (2014)

## Abstract (arXiv)

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.