DETERMINING PERFORMANCE OF A MACHINE-LEARNING MODEL BASED ON AGGREGATION OF FINER-GRAIN NORMALIZED PERFORMANCE METRICS
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United States of America Patent
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Issued Date -
Aug 2, 2018
app pub date -
Feb 1, 2017
filing date -
Feb 1, 2017
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Abstract
An online system receives content items, for example, from content providers and sends the content items to users. The online system uses machine-learning models for predicting whether a user is likely to interact with a content item. The online system uses stored user interactions to measure the model performance to determine whether the model can be used online. The online system determines a baseline model using stored user interactions. The online system determines whether the machine-learning model performs better than the baseline model or worse for each content provider. The online system determines whether to approve the model for online use based on an aggregate normalized performance metric, for example, a metric representing the fraction of content providers for which the model performs better than the baseline. If the online system determines to reject the model, the online system retrains the model.
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