Invention Application
- Patent Title: EFFICIENTLY DETERMINING LOCAL MACHINE LEARNING MODEL FEATURE CONTRIBUTIONS
-
Application No.: US16520645Application Date: 2019-07-24
-
Publication No.: US20210027191A1Publication Date: 2021-01-28
- Inventor: Ritwik Sinha , Sunny Dhamnani , Moumita Sinha
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06F16/904

Abstract:
The present disclosure relates to a feature contribution system that accurately and efficiently provides the influence of features utilized in machine-learning models with respect to observed model results. In particular, the feature contribution system can utilize an observed model result, initial contribution values, and historical feature values to determine a contribution value correction factor. Further, the feature contribution system can apply the correction factor to the initial contribution values to determine correction-factor adjusted contribution values of each feature of the model with respect to the observed model result.
Public/Granted literature
- US11995520B2 Efficiently determining local machine learning model feature contributions Public/Granted day:2024-05-28
Information query