Undersampling is a technique for addressing class imbalance by reducing the number of examples from an overrepresented class. It can make the training set more balanced, but may discard useful information if too many examples are removed.
Removing examples from the majority class in a class-imbalanced dataset in order to create a more balanced training set. For example, consider a dataset in which the ratio of the majority class to the minority class is 20:1. To overcome this class imbalance, you could create a training set consisting of all of the minority class examples but only a tenth of the majority class examples, which would create a training-set class ratio of 2:1. Thanks to undersampling, this more balanced training set might produce a better model. Alternatively, this more balanced training set might contain insufficient examples to train an effective model. Contrast with oversampling.