Data Augmentation

Data Augmentation refers to a suite of techniques used to artificially increase the amount of data by creating slightly modified copies of already existing data points. This strategy is particularly vital in machine learning, especially when working with limited datasets, as it helps to prevent overfitting and improve the generalization capability of models. By introducing variations into the training data, augmentation encourages the model to learn more robust features and become less sensitive to minor perturbations, ultimately leading to better performance on unseen data.