INLINE VALIDATION OF MACHINE LEARNING MODELS

    公开(公告)号:US20230118341A1

    公开(公告)日:2023-04-20

    申请号:US17502536

    申请日:2021-10-15

    Abstract: Methods, apparatuses, and computer readable media are disclosed. An application server may receive a dataset that includes records associated with user device interactions with a computer system. The application server may modify one or more records according to a data modification metric. The modifying may result in a modified dataset that satisfies a similarity metric defining a permissible deviation between the received dataset and the modified dataset according to a deviation threshold. The data modification metric may satisfy the similarity metric and may define a deviation in the modified dataset that results in an expected classification by the machine learning predictive model to classify the deviation in the modified dataset as an outlier event. The application server may process the modified dataset with the machine learning predictive model to produce a result. The application server may compare the expected classification to the classification to validate the model.

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