Oversampling is a technique for class-imbalanced datasets that reuses or increases examples from a minority class to make training more balanced. It can improve learning for rare classes but may increase the risk of overfitting if repeated examples are memorized.
Reusing the examples of a minority class in a class-imbalanced dataset in order to create a more balanced training set. For example, consider a binary classification problem in which the ratio of the majority class to the minority class is 5,000:1. If the dataset contains a million examples, then the dataset contains only about 200 examples of the minority class, which might be too few examples for effective training. To overcome this deficiency, you might oversample (reuse) those 200 examples multiple times, possibly yielding sufficient examples for useful training. You need to be careful about over overfitting when oversampling. Contrast with undersampling.