View source for 18210756. MODEL-AGNOSTIC MULTI-FACTOR METRIC DRIFT ATTRIBUTION (Adobe Inc.)

=MODEL-AGNOSTIC MULTI-FACTOR METRIC DRIFT ATTRIBUTION=


==Organization Name==

[[:Category:Adobe Inc.|Adobe Inc.]]


[[Category:Adobe Inc.]]

==Inventor(s)==

[[:Category:Nimish Srivastav of Santa Clara CA (US)|Nimish Srivastav of Santa Clara CA (US)]][[Category:Nimish Srivastav of Santa Clara CA (US)]]

[[:Category:Vijay Srivastava of San Jose CA (US)|Vijay Srivastava of San Jose CA (US)]][[Category:Vijay Srivastava of San Jose CA (US)]]

[[:Category:Deepak Pai of Sunnyvale CA (US)|Deepak Pai of Sunnyvale CA (US)]][[Category:Deepak Pai of Sunnyvale CA (US)]]

==MODEL-AGNOSTIC MULTI-FACTOR METRIC DRIFT ATTRIBUTION==


This abstract first appeared for US patent application 18210756 titled 'MODEL-AGNOSTIC MULTI-FACTOR METRIC DRIFT ATTRIBUTION




==Original Abstract Submitted==

Multi-factor metric drift evaluation and attribution techniques are described. A drift attribution model is trained to compute, for a segment of input data that defines an observed value for a metric and observed values for each of a plurality of factors that influence the value of the metric, a contribution by each of the plurality of factors to the observed metric value. Drift observations output by the trained drift attribution model are further processed using a Shapely explainer to represent contributions of each of the metric factors, and their associated values, relative to one or more observed values of a metric during the time segment. The respective magnitude by which each metric factor affects an observed value of the metric is described in a metric drift report, which objectively quantifies respective impacts of a factor, relative to other factors that affect a metric.



[[Category:G06N20/00]]
[[Category:CPC_G06N20/00]]