Amazon Technologies, Inc.

Denis V Batalov

David R Azari

Alan Kipust

Tanvi Milind Bhadbhade

Lee M Miller

Theresa M Hollis

A technology is provided for automated tuning of a machine learning model in a computing service environment. Predictive weights that include false positive outcomes, false negative outcomes, true positive outcomes, and true negative outcomes may be defined and/or received. A weight adjusted classification threshold, for use in a classification model of the machine learning model in a service provider environment, according to the predictive weights to enable the machine learning model to increase the total value of the machine learning model and decrease performance outcome errors. The improved classification threshold may be adjusted according to a change in the predictive weights. A data point may be classified according to the weight adjusted classification threshold in the classification model.

Tuning of machine learning models using accuracy metrics selected to increase performance

When a digital item is submitted for publication, an automated system may determine whether the digital item includes content from other digital items. In some implementations, when the digital item is an electronic book (eBook), the automated system may select sets of words from the eBook and compute hash codes, such that each hash code corresponds to a set of words. The automated system may compare the computed hash codes with retained hash codes associated with other electronic books to determine whether the digital item includes duplicate content.

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Luxembourg, Luxembourg

Denis V Batalov