Systems and methods for mitigating training data baseline shifts in artificial intelligence models comprising moving classifications

Abstract

The system generates multiple classes of training data depending on their baseline shift. The system then trains a model or ensemble of models leveraging the entire dataset without the need for training them separately. A set of uniform input feature are generated for a user based on profile of the data (e.g., based on a user's auto loan terms and/or credit history). The system then engineers additional characteristic features based on the time criteria (e.g., based on a time period overlap with the time period of the classes and what class the profile data originated from). The system uses this time criteria (e.g., whether profile data originated from a given class and the amount of overlap of the data with a class) to determine how the feature input is processed through the models.