What technique is used to artificially increase the amount of data by generating new data points from existing data?

Data augmentation is a key technique in machine learning that involves creating new data points from existing data to artificially expand the size of a dataset. This is particularly valuable in scenarios where obtaining more data is expensive or infeasible. By applying various transformations, such as rotations, translations, flipping, zooming, or adding noise, you can enhance the variability of the training set, which helps the model generalize better to unseen data during inference.

Using data augmentation can mitigate overfitting by providing a more diverse set of examples, enabling the model to learn more robust features. For instance, in image classification tasks, augmenting images helps the model recognize objects in various orientations or lighting conditions, improving its performance.

The other options listed—data normalization, data compression, and data transformation—address different aspects of data handling. Normalization involves scaling features to a similar range to improve model training, compression reduces the size of the data for storage or speed, and transformation refers to altering data formats or structures without fundamentally generating new samples. However, none of these techniques are aimed specifically at increasing the dataset size through the creation of new data points.