Invention Grant
- Patent Title: Determining feature impact within machine learning models using prototypes across analytical spaces
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Application No.: US16253892Application Date: 2019-01-22
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Publication No.: US11580420B2Publication Date: 2023-02-14
- Inventor: Deepak Pai , Joshua Sweetkind-Singer , Debraj Basu
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Keller Preece PLLC
- Main IPC: G06N5/04
- IPC: G06N5/04 ; G06N20/00

Abstract:
Methods, systems, and non-transitory computer readable storage media are disclosed for analyzing feature impact of a machine-learning model using prototypes across analytical spaces. For example, the disclosed system can identify features of data points used to generate outputs via a machine-learning model and then map the features to a feature space and the outputs to a label space. The disclosed system can then utilize an iterative process to determine prototypes from the data points based on distances between the data points in the feature space and the label space. Furthermore, the disclosed system can then use the prototypes to determine the impact of the features within the machine-learning model based on locally sensitive directions; region variability; or mean, range, and variance of features of the prototypes.
Public/Granted literature
- US20200234158A1 DETERMINING FEATURE IMPACT WITHIN MACHINE LEARNING MODELS USING PROTOTYPES ACROSS ANALYTICAL SPACES Public/Granted day:2020-07-23
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