Label shift detection and adjustment in predictive modeling
US11599746B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 30, 2020 |
| Grant date | Mar 7, 2023 |
| Priority date | — |
| Expiry date | Apr 22, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/751
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for detecting label shift and adjusting training data of predictive models in response are provided. In an embodiment, a first machine-learned model is used to generate a predicted label for each of multiple scoring instances. The first machine-learned model is trained using one or more machine learning techniques based on a plurality of training instances, each of which includes an observed label. In response to detecting a shift in observed labels, for each segment of one or more segments in multiple segments, a portion of training data that corresponds to the segment is identified. For each training instance in a subset of the portion of training data, the training instance is adjusted. The adjusted training instance is added to a final set of training data. The machine learning technique(s) are used to train a second machine-learned model based on the final set of training data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.