Automated artificial intelligence model generation, training, and testing

Abstract

Mechanisms are provided to automatically generate a machine learning (ML) computer model. The mechanisms automatically generate a plurality of aggregated dataset groups, each having original dataset(s) grouped together based on a degree of correlation between characteristics of each of the original datasets. The mechanisms automatically generate, for each aggregated dataset group, a plurality of ML computer model instances, each being a ML computer model configured with a different combination of thresholds and hyperparameters than other ML computer model instances. The plurality of ML computer model instances are executed to generate performance metric information for each ML computer model instance. The performance metric information is analyzed to select a set of ML computer model instances for the aggregated dataset. The mechanisms select one or more ML computer model instances from across all of the sets of ML computer model instances as a candidate for deployment to a decision support computing system.