Introspection of machine learning estimations
US11868852B1 · kind B1 · utility
Assignee
Inventor
Key dates
| Filing date | May 4, 2017 |
| Grant date | Jan 9, 2024 |
| Priority date | — |
| Expiry date | Feb 11, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L63/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A machine learning algorithm, such as a random forest regressor, can be trained using a set of annotated data objects to estimate the risk or business value for an object. The feature contributions for each data object can be analyzed and a representation generated that clusters data objects by feature contributions. Any clustering of data objects with incorrect scores in the visualization can be indicative of gaps in the regressor training. Adjustments to the inputs can be made, and the regressor retrained, to eliminate clustering of errors for similar feature contributions. Correcting the risk score estimations can ensure that the appropriate security policies and permissions are applied to each data object.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.