Abstract: Methods for a machine-learning network that provide efficient, scalable, and granular analyses during validation of a machine learning model are disclosed. Providing quantitative analysis information to machine learning experts when they are deciding how to proceed with further optimizing their given machine learning model allows for more directed procedures during edge case detection. Following the execution of a principal machine learning model using a validation dataset, a shallow learning model may be trained to provide simulations about how the principal model may be improved, or not, given different re-training scenarios. By using slice-based schemes, the validation dataset is divided into certain problematic data slices, and then, during inference of the shallow learning model, additional quantitative information about the effect on other data slices given a subsequent re-training of the principal model using a certain problematic data slice allows the ML expert to make more informed decisions.

Abstract: Methods for a machine-learning network that provide efficient, scalable, and granular analyses during validation of a machine learning model are disclosed. Validation of models depends upon many factors, including the real-world application of the model, the type of model being trained, and the types of data samples it is being trained on. In order to provide relevant edge case information to users that pertains to their specific model, data slice finding techniques may be used to identify subsets of the dataset that are particularly problematic. By limiting a length of the slice description that the algorithm searches and by configuring the algorithm to target specific types of errors, users are provided with a more granular analysis that then allows them to determine how or if they need to retrain the model.