oversampling takes samples faster than the minimum rate required by the desired signal bandwidth. With suitable uncorrelated conversion noise, digital filtering removes noise outside that bandwidth before decimation. Independent-error averaging improves standard deviation as ; fourfold averaging ideally gains one bit of amplitude resolution. Repetition of an unchanged biased code does not produce the same benefit.
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Oversampling is a technique used in data processing, particularly in the context of imbalanced datasets, where one class (or category) is significantly overrepresented compared to others. This imbalance can negatively affect the performance of machine learning models, as they may become biased towards the majority class and fail to learn the characteristics of the minority class effectively. In oversampling, instances of the minority class are artificially increased to balance the ratio between the minority and majority classes.