What does "data augmentation" refer to in the context of machine learning?

Data augmentation refers to techniques employed to increase the variety and quantity of training data by creating modified versions of existing data without the need to collect new data. This is particularly important in the context of training machine learning models, as having a larger and more diverse dataset can significantly enhance model performance.

For instance, in the realm of image processing, data augmentation might involve transformations such as rotations, flips, scaling, or color adjustments to existing images. By applying these transformations, a model can become more robust to variations it may encounter in real-world applications, improving its generalization capabilities and reducing the risk of overfitting.

The other options represent different concepts in machine learning. Minimizing training time pertains to optimization techniques or strategies for model acceleration, irrelevant to data augmentation. Selecting the most relevant features involves feature selection, which is crucial for improving a model’s performance but does not relate to augmenting training data. Enhancing the quality of label data refers to data cleaning and preprocessing, which focuses on improving data quality rather than increasing its size or diversity.