Machine learning model optimization explainability
AssigneeC3.ai, Inc.
InventorsFabian Rigterink, Zhaoxi Zhang, Zhaoyang Jin, Utsav Dutta
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Abstract
Machine learning model optimization explainability application is provides explanations (e.g., natural language explanations) for the operations and decisions associated with an optimization model (e.g., optimization algorithm) used to solve an optimization problem. More specifically, the software application for machine learning model optimization explainability enables explainability for a query that determine how the solution was generated. The system can provide a query response (e.g., natural language explanation), and perform a variety of different actions to address any issues surfaced in the query response.
Claims22
A method comprising:
- generating, by a processor, a dependency graph based on an optimization model;
- traversing a path of the dependency graph based on a query;
- reviewing, by the processor, the traversed path of the dependency graph;
- generating, by the processor, explanation information, based on reviewing the path of the dependency graph, that explains decisions related to an issue identified in the query;
- and
- executing instructions for a corrective action based on the explanation information and the optimization model.
A method comprising:
- providing a query regarding a solution to an optimization problem;
- receiving, based on the query, explanation information associated with an optimization model used to solve the optimization problem, wherein the explanation information explains decisions related to the solution of the optimization problem;
- and
- executing instructions for a corrective action includes transmitting one or more control signals for based on the optimization model and the explanation information.
Dependencies are inferred from the claim text.