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公开(公告)号：US20220012640A1
公开(公告)日：2022-01-13
申请号：US16924934
申请日：2020-07-09
发明人： Arun Kwangil IYENGAR , Jeffrey Owen KEPHART , Dhavalkumar C. PATEL , Dung Tien PHAN , Chandrasekhara K. REDDY
摘要： Techniques for model evaluation and selection are provided. A plurality of models trained to generate predictions at each of a plurality of intervals is received, and a plurality of model ensembles, each specifying one or more of the plurality of models for each of the plurality of intervals, is generated. A test data set is received, where the test data set includes values for at least a first interval of the plurality of intervals and does not include values for at least a second interval of the plurality of intervals. A first model ensemble, of the plurality of model ensembles, is selected based on processing the test data set using each of the plurality of model ensembles.
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公开(公告)号：US20220012641A1
公开(公告)日：2022-01-13
申请号：US16925013
申请日：2020-07-09
发明人： Arun Kwangil IYENGAR , Jeffrey Owen KEPHART , Dhavalkumar C. PATEL , Dung Tien PHAN , Chandrasekhara K. REDDY
摘要： Techniques for generating model ensembles are provided. A plurality of models trained to generate predictions at each of a plurality of intervals is received. A respective prediction accuracy of each respective model of the plurality of models is determined for a first interval of the plurality of intervals by processing labeled evaluation data using the respective model. Additionally, a model ensemble specifying one or more of the plurality of models for each of the plurality of intervals is generated, comprising selecting, for the first interval, a first model of the plurality of models based on (i) the respective prediction accuracies and (ii) at least one non-error metric.
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