Invention Grant
- Patent Title: Data drift impact in a machine learning model
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Application No.: US17548070Application Date: 2021-12-10
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Publication No.: US12056586B2Publication Date: 2024-08-06
- Inventor: Jason Lopatecki , Aparna Dhinakaran , Michael Schiff
- Applicant: ARIZE AI, INC.
- Applicant Address: US CA Mill Valley
- Assignee: ARIZE AI, INC.
- Current Assignee: ARIZE AI, INC.
- Current Assignee Address: US CA Mill Valley
- Agency: DLA Piper LLP (US)
- Main IPC: G06N20/00
- IPC: G06N20/00

Abstract:
Techniques for determining a drift impact score in a machine learning model are disclosed. The techniques can include: obtaining a reference distribution of a machine learning model; obtaining a current distribution of the machine learning model; determining a statistical distance based on the reference distribution and the current distribution; determining a local feature importance parameter for each feature associated with a prediction made by the machine learning model; determining a cohort feature importance parameter for a cohort of multiple features based on the local feature importance parameter of each feature in the cohort; and determining a drift impact score for the cohort based on the statistical distance and the cohort feature importance parameter.
Public/Granted literature
- US20230186144A1 Data Drift Impact In A Machine Learning Model Public/Granted day:2023-06-15
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