# An Exploration of How Training Set Composition Bias in Machine Learning Affects Identifying Rare Objects

arXiv: https://arxiv.org/abs/2207.03207 · HTML: https://mlsanity.com/paper/2207.03207/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 59 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Understanding Random Forests: From Theory to Practice](https://mlsanity.com/paper/1407.7502/) (2014)
- [Adversarial Examples in Modern Machine Learning: A Review](https://mlsanity.com/paper/1911.05268/) (2019)

## Built on by (most cited first, 1 of 1)

- [The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review](https://mlsanity.com/paper/2402.13635/) (2024)

## Abstract (arXiv)

When training a machine learning classifier on data where one of the classes is intrinsically rare, the classifier will often assign too few sources to the rare class. To address this, it is common to up-weight the examples of the rare class to ensure it isn't ignored. It is also a frequent practice to train on restricted data where the balance of source types is closer to equal for the same reason. Here we show that these practices can bias the model toward over-assigning sources to the rare class. We also explore how to detect when training data bias has had a statistically significant impact on the trained model's predictions, and how to reduce the bias's impact. While the magnitude of the impact of the techniques developed here will vary with the details of the application, for most cases it should be modest. They are, however, universally applicable to every time a machine learning classification model is used, making them analogous to Bessel's correction to the sample variance.