Feature contributors and influencers in machine learned predictive models
US11250340B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 14, 2017 |
| Grant date | Feb 15, 2022 |
| Priority date | — |
| Expiry date | Nov 27, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q50/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In an example, for each feature of one or more features of a target sample data, feature values for one or more pseudo-samples are generated using, localized stratified sampling. The one or more pseudo-samples are fed into the trained machine learned model to obtain their prediction values. A piecewise linear regression model is trained using the one or more pseudo-samples and their prediction values, the piecewise linear regression model having two coefficients for each feature, a first coefficient describing prediction change when a corresponding feature value is increased and a second coefficient describing prediction change when a corresponding feature value is decreased. A top positive feature influencer is identified based on a feature of the one or more features of the target sample having a greatest magnitude of positive first coefficient or greatest magnitude of negative second coefficient. A top negative feature influencer is identified based on a feature of the one or more features of the target sample having a greatest magnitude of negative first coefficient or greatest magnitude of positive second coefficient. A top feature contributor is identified based on a feature …
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.