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公开(公告)号：US11599746B2
公开(公告)日：2023-03-07
申请号：US16916706
申请日：2020-06-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jilei Yang , Yu Liu , Parvez Ahammad , Fangfang Tan
Abstract: Techniques for detecting label shift and adjusting training data of predictive models in response are provided. In an embodiment, a first machine-learned model is used to generate a predicted label for each of multiple scoring instances. The first machine-learned model is trained using one or more machine learning techniques based on a plurality of training instances, each of which includes an observed label. In response to detecting a shift in observed labels, for each segment of one or more segments in multiple segments, a portion of training data that corresponds to the segment is identified. For each training instance in a subset of the portion of training data, the training instance is adjusted. The adjusted training instance is added to a final set of training data. The machine learning technique(s) are used to train a second machine-learned model based on the final set of training data.
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公开(公告)号：US20210406598A1
公开(公告)日：2021-12-30
申请号：US16916706
申请日：2020-06-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jilei Yang , Yu Liu , Parvez Ahammad , Fangfang Tan
Abstract: Techniques for detecting label shift and adjusting training data of predictive models in response are provided. In an embodiment, a first machine-learned model is used to generate a predicted label for each of multiple scoring instances. The first machine-learned model is trained using one or more machine learning techniques based on a plurality of training instances, each of which includes an observed label. In response to detecting a shift in observed labels, for each segment of one or more segments in multiple segments, a portion of training data that corresponds to the segment is identified. For each training instance in a subset of the portion of training data, the training instance is adjusted. The adjusted training instance is added to a final set of training data. The machine learning technique(s) are used to train a second machine-learned model based on the final set of training data.
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