- Patent Title: Prediction characterization for black box machine learning models
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Application No.: US16124047Application Date: 2018-09-06
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Publication No.: US11328220B2Publication Date: 2022-05-10
- Inventor: Charles Parker
- Applicant: BigML, Inc.
- Applicant Address: US OR Corvallis
- Assignee: BigML, Inc.
- Current Assignee: BigML, Inc.
- Current Assignee Address: US OR Corvallis
- Agency: FisherBroyles, LLP
- Agent Jeremy P. Sanders
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06N5/04 ; G06N5/00

Abstract:
A non-transitory computer-readable medium including instructions, which when executed by one or more processors of a computing system, causes the computing system to: access a machine learning model m, an input data point P to m, P including one or more features, and a prediction m(P) of m for P; create a set of perturbed input data points Pk from P by selecting a new value for at least one feature of P for each perturbed input data point; obtain a prediction m(Pk) for each of the perturbed input data points; analyze the predictions m(Pk) for the perturbed input data points to determine which features are most influential to the prediction; and output the analysis results to a user.
Public/Granted literature
- US20190122135A1 PREDICTION CHARACTERIZATION FOR BLACK BOX MACHINE LEARNING MODELS Public/Granted day:2019-04-25
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