Data augmentation: Regularization method used to decrease the model's variance error consisting in artificially increasing the number and variance of training samples by transforming existing samples to create additional samples. For example, if images are one of the system features, data augmentation can rotate, crop, and reflect each image to produce many variants of the original, yielding more variate labeled data to decrease the model's error.