公开(公告)号：US12051008B2

公开(公告)日：2024-07-30

申请号：US17883503

申请日：2022-08-08

Applicant: Salesforce, Inc.

Inventor： Donglin Hu ,  Yuxi Zhang ,  Kexin Xie ,  Chen Xu

IPC: G06N5/022 ,  G06Q30/0241

CPC classification number: G06N5/022 ,  G06Q30/0241

Abstract: A prediction system of an online system deploys one or more machine-learned architectures to generate predictions. In one embodiment, the machine-learned architecture is a stacked ensemble model. The stacked ensemble model includes a plurality of base models, where a base model is coupled to receive input data and generate a base prediction for the input data. The stacked ensemble model includes a meta model that combines the base predictions to generate a meta prediction for the input data. The prediction system also generates a reliability measure that takes advantage of the base predictions to evaluate the reliability of the meta prediction. In this manner, while the quality of individual predictions may differ from one another depending on the values of the input data, the prediction system can dynamically generate the reliability measure to account for this variation.

公开(公告)号：US20240046115A1

公开(公告)日：2024-02-08

申请号：US17883503

申请日：2022-08-08

Applicant: Salesforce, Inc.

Inventor： Donglin Hu ,  Yuxi Zhang ,  Kexin Xie ,  Chen Xu

IPC: G06N5/02 ,  G06Q30/02

CPC classification number: G06N5/022 ,  G06Q30/0241

Abstract: A prediction system of an online system deploys one or more machine-learned architectures to generate predictions. In one embodiment, the machine-learned architecture is a stacked ensemble model. The stacked ensemble model includes a plurality of base models, where a base model is coupled to receive input data and generate a base prediction for the input data. The stacked ensemble model includes a meta model that combines the base predictions to generate a meta prediction for the input data. The prediction system also generates a reliability measure that takes advantage of the base predictions to evaluate the reliability of the meta prediction. In this manner, while the quality of individual predictions may differ from one another depending on the values of the input data, the prediction system can dynamically generate the reliability measure to account for this variation.