Abstract: A system can include a data processing system that can include memory and one or more processors to generate, by a first model trained using machine learning and compatible with first data having a first type and second data having a second type, a first metric based on the first data and indicating a first fault probability in a second model, generate, by the first model, a second metric based on the second data and indicating a second fault probability in a third model, determine, based on the first metric and the second metric, that an aggregate model that includes the second model and the third model satisfies a heuristic indicating a third fault probability in the aggregate model, and instruct, in response to a determination that the aggregate model satisfies the heuristic, a user interface to present an indication that the aggregate model satisfies the heuristic.

Abstract: The methods and systems for generating recipes are disclosed. One aspect of the invention is directed toward a method for generating a recipe that includes receiving a request for a recipe recommendation, collecting customer information, and providing a recipe based on the customer information, a defined promotional strategy, or relationship between the purchase of selected food items. Further aspects of the invention can include providing a list of food items related to the recipe, generating a store map with the location of food items, providing discount incentives for food items related to the recipe, and generating a shopping sequence for collecting food items related to the recipe.