What advantage does data augmentation provide in the context of machine learning?

Data augmentation is a valuable technique in machine learning that enhances the robustness and performance of models, particularly in scenarios where the available training data is limited. By artificially expanding the training dataset through various transformations—such as rotation, scaling, flipping, or adding noise—augmented data helps the model learn more generalized features. This increased variety in the training samples allows the model to better handle variations it may encounter in real-world data.

As a result, the model becomes less sensitive to specific examples and more adaptable to unseen instances. This leads to improved accuracy and a lower likelihood of overfitting, as the model learns to recognize patterns rather than memorizing the training data. Overall, the advantage of data augmentation lies in its ability to enhance the performance and reliability of machine learning models, making them better suited for practical applications.