What methods are used to enhance the quantity or diversity of data sets through transformations?

Data augmentation refers to a set of techniques employed to increase the quantity and diversity of data sets, particularly in the context of training machine learning models. This method involves applying various transformations to existing data samples to create new, modified versions. For example, in image classification tasks, data augmentation can include techniques such as flipping, rotating, cropping, or altering the brightness of images. By generating these variations, data augmentation helps improve the robustness of models, allowing them to generalize better when exposed to unseen data.

In contrast, data scaling and normalization focus primarily on adjusting the range or distribution of numerical values within a dataset rather than creating new data instances. Data integration pertains to combining data from different sources to create a comprehensive dataset, which addresses issues related to data completeness and consistency but does not directly enhance the size or diversity of individual data samples. Therefore, data augmentation stands out as the primary method for effectively enhancing both the quantity and diversity of data sets via transformations.