Model training method and apparatus based on gradient boosting decision tree
US11157818B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 26, 2021 |
| Grant date | Oct 26, 2021 |
| Priority date | — |
| Expiry date | Jan 26, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/027
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed are a model training method and apparatus based on gradient boosting decision tree (GBDT). A GBDT algorithm flow is divided into two stages. In the first stage, labeled samples are obtained from a data domain of a service scenario similar to a target service scenario to sequentially train several decision trees, and training residual generated after the training in the first stage is determined; in the second stage, labeled samples are obtained from a data domain of the target service scenario, and several decision trees continue to be trained based on the training residual. Finally, a model applied to the target service scenario is actually obtained by integrating the decision trees trained in the first stage with the decision trees trained in the second stage.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.