Systems and methods for generating synthetic training datasets for training machine learning models in training data-sparse environments for non-homogenous predictions

Abstract

In some embodiments, generating synthetic training datasets for training machine learning models in training data-sparse environments for non-homogenous predictions may be facilitated. In some embodiments, user-specific information associated with a user may be received. The system may generate synthetic training data representing one or more alternative actions corresponding to one or more characteristics by: labeling the user-specific information, determining (e.g., based on the labeled user-specific information) one or more characteristics of the labeled user-specific information, and determining (e.g., based on the one or more characteristics) one or more alternative actions corresponding to the one or more characteristics. The system may then train a machine learning model based on the synthetic training data to generate a prediction in response to providing an action of a first user to the machine learning model.