Data Augmentation for Regression I want to use deep learning for regression. However, the number of training samples is not large. In image processing, some new samples are generated on the basis of initial data through tasks like rotation to increase the number of data sets. Can I know if there is any algorithm to generate more data based on current samples (data augmentation for regression)?
Yes, you can perturb your data (and targets) in ways that you wish your model to be robust against, for example by adding small amounts of noise (possibly Gaussian) or synthetic anomalies, or by creating meaningful aggregates. Some models also improve just by running the same training data through them more than once, randomly shuffled (“epochs”).