Systems and methods for efficient test-time prediction of model arbitrariness

Abstract

Systems and methods for efficient test-time prediction of model arbitrariness are disclosed. According to an embodiment, a method for efficient test-time estimation of predictive multiplicity may include: (1) receiving, by arbitrariness prediction computer program, a trained machine learning model, wherein the trained machine learning model comprises a plurality of nodes, and each node has a weight; (2) determining, by the arbitrariness prediction computer program, a number of dropout models for the trained machine learning model to generate; (3) creating, by the arbitrariness prediction computer program, the number of dropout models; (4) providing, by the arbitrariness prediction computer program, sample data to each of the dropout models; (5) receiving, by the arbitrariness prediction computer program, an output from each of the dropout models; and (6) determining, by the arbitrariness prediction computer program, an arbitrariness for the trained machine learning model based on the outputs.

Dependencies are inferred from the claim text.