Which strategy helps to improve diversity in training data?

Data augmentation is a key technique for improving the diversity of training data. This strategy involves creating variations of existing data samples to increase the overall dataset size and variety without the need for new data collection. By applying transformations such as rotation, flipping, scaling, and color adjustment to images, or generating synthetic examples in text or audio datasets, data augmentation can help models generalize better and become more robust to different scenarios.

When a model is trained on a more diverse dataset, it becomes less likely to overfit to any specific examples, as it learns to recognize patterns that are applicable across a wider range of inputs. This is particularly useful in fields where collecting additional data is difficult, costly, or time-consuming. Thus, data augmentation plays a crucial role in enhancing the machine learning model's ability to navigate and perform well on various unseen data points that it may encounter in the real world.