What is the main purpose of data augmentation in machine learning?

The main purpose of data augmentation in machine learning is to create new training samples by altering existing ones. This process is crucial for enhancing the training dataset, especially when the original dataset is limited or imbalanced. By applying various transformations—such as rotation, flipping, cropping, scaling, or adding noise—data augmentation generates synthetic variations of the images or data points, effectively increasing the diversity of the training set. This technique helps improve the robustness and generalization capability of the model by exposing it to a wider range of possibilities during training. As a result, models can achieve better performance on unseen data.

The approach does not primarily focus on improving computational efficiency, reducing the amount of training data needed, or enhancing interpretability. While these aspects are important in the broader context of machine learning, the core goal of data augmentation is firmly rooted in expanding and enriching the training data itself. This strategy mitigates overfitting and allows the model to learn more effectively from the available information.