GENERALIZED NONLINEAR MIXED EFFECT MODELS VIA GAUSSIAN PROCESSES

    公开(公告)号:US20200380407A1

    公开(公告)日:2020-12-03

    申请号:US16430243

    申请日:2019-06-03

    Abstract: In an example embodiment, training data is obtained, the training data comprising values for a plurality of different features. Then a global machine learned model is trained using a first machine learning algorithm by feeding the training data into the first machine learning algorithm during a fixed effect training process. A non-linear first random effects machine learned model is trained by feeding a subset of the training data into a second machine learning algorithm, the subset of the training data being limited to training data corresponding to a particular value of one of the plurality of different features.

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