Assessing accuracy of a machine learning model
US12073292B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 24, 2017 |
| Grant date | Aug 27, 2024 |
| Priority date | — |
| Expiry date | Jun 15, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0202
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Balancing content distribution between a machine learning model and a statistical model provides a baseline assurance in combination with the benefits of a well-trained machine learning model for content selection. In some implementations, a server receiving requests for a content item assigns a first proportion of the received requests to a first group and assigns remaining requests to a second group. The server uses a machine learning model to select variations of the requested content item for responding to requests assigned to the first group and uses a statistical model to select content variations for requests assigned to the second group. The server obtains performance information, e.g., acceptance rates for the different variations, and compares performance of the different models used for content selection. Audience share assigned to the machine learning model is increased when it outperforms the statistical model and decreased when it underperforms the statistical model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.