# Minority class (machine learning)

> The less common label in aclass-imbalanced dataset. For example, given a dataset containing 99% negative labels and 1% positive labels, the positive labels are the minority class.[^1]

The less common [label](https://wiki.g15e.com/pages/Label%20(machine%20learning.txt)) in a[class-imbalanced dataset](https://wiki.g15e.com/pages/Class-imbalanced%20dataset.txt). For example, given a dataset containing 99% negative labels and 1% positive labels, the positive labels are the minority class.[^1]

## Notes

A [training set](https://wiki.g15e.com/pages/Training%20data.txt) with a million [examples](https://wiki.g15e.com/pages/Example%20(machine%20learning.txt)) sounds impressive. However, if the minority class is poorly represented, then even a very large training set might be insufficient. Focus less on the total number of examples in the dataset and more on the number of examples in the minority class.[^1]

If your dataset doesn't contain enough minority class examples, consider using [downsampling](https://wiki.g15e.com/pages/Downsampling%20(machine%20learning.txt)) to supplement the minority class.[^1]

## See also

- [Majority class](https://wiki.g15e.com/pages/Majority%20class%20(machine%20learning.txt))

## Footnotes

[^1]: https://developers.google.com/machine-learning/glossary#minority_class