Answer / Shamendra

Data augmentation involves creating new synthetic training samples by applying various transformations to existing samples, thereby increasing the size of the dataset and helping to prevent overfitting. Examples of data augmentation techniques include rotation, flipping, zooming, and adding noise for image data; and permutation, scaling, and adding noise for tabular data.

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