Oversampling and undersampling in data analysis

This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these template messages) This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Find sources: "Oversampling and undersampling in data analysis" – news · newspapers · books · scholar · JSTOR (April 2011) (Learn how and when to remove this message) This article includes a list of general references, but it lacks sufficient corresponding inline citations. Please help to improve this article by introducing more precise citations. (April 2011) (Learn how and when to remove this message) (Learn how and when to remove this message)

Within statistics, oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set (i.e. the ratio between the different classes/categories represented). These terms are used both in statistical sampling, survey design methodology and in machine learning.

Oversampling and undersampling are opposite and roughly equivalent techniques. There are also more complex oversampling techniques, including the creation of artificial data points with algorithms like Synthetic minority oversampling technique.^[1][2]