GENERATING QUANTITATIVELY ASSESSED SYNTHETIC TRAINING DATA

In an approach to generating quantitatively assessed synthetic training data, one or more computer processors identify an initial plurality of clusters in a dataset utilizing a trained classification model and a plurality of associated hyperparameters, wherein the clusters have sufficient density to be represented in a calculated probability distribution. The one or more computer processors generate one or more synthetic data points for each identified cluster utilizing a corresponding calculated probability distribution. The one or more computer processors quantitatively assess the one or more generated synthetic data points.