Predictive models having decomposable hierarchical layers configured to generate interpretable results
US11593680B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 14, 2020 |
| Grant date | Feb 28, 2023 |
| Priority date | — |
| Expiry date | Jun 21, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer-implemented method for providing interpretable predictions from a machine learning model includes receiving a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y). An interpretability model is built corresponding to the predictive models, by assigning an interpretability to each prediction Yi based on the hierarchical structure. Assigning the interpretability includes decomposing X into a plurality of partitions Xj using the hierarchical structure, wherein X=U1NXj, N being the number of partitions. Further, each partition is decomposed into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained. A score is computed for each partition as a function of the predicted scores of the sub-partitions, wherein the predicted scores represent interactions between the sub-partitions. Further, an interpretation of a prediction is outputted.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.