US Patent No. 10,169,715

FEATURE PROCESSING TRADEOFF MANAGEMENT


Patent No. 10,169,715
Issue Date January 01, 2019
Title Feature Processing Tradeoff Management
Inventorship Leo Parker Dirac, Seattle, WA (US)
Nicolle M. Correa, Seattle, WA (US)
Charles Eric Dannaker, Seattle, WA (US)
Assignee Amazon Technologies, Inc., Reno, NV (US)

Claim of US Patent No. 10,169,715

1. A system, comprising:one or more computing devices configured to:
determine, via one or more programmatic interactions with a client of a machine learning service of a provider network, (a) one or more target variables to be predicted using a specified training data set, (b) one or more prediction quality metrics including a particular prediction quality metric, and (c) one or more prediction run-time goals including a particular prediction run-time goal;
identify a set of candidate feature processing transformations to derive a first set of processed variables from one or more input variables of the specified data set, wherein at least a subset of the first set of processed variables is usable to train a machine learning model to predict the one or more target variables, and wherein the set of candidate feature processing transformations includes a particular feature processing transformation;
determine (a) a quality estimate indicative of an effect, on the particular prediction quality metric, of implementing the particular candidate feature processing transformation, and (b) a cost estimate indicative of an effect, on a particular run-time performance metric associated with the particular prediction run-time goal, of implementing the particular candidate feature processing transformation;
generate, based at least in part on the quality estimate and at least in part on the cost estimate, a feature processing proposal to be provided to the client for approval, wherein the feature processing proposal includes a recommendation to implement the particular feature processing transformation; and
in response to an indication of approval from the client, execute a machine learning model trained using a particular processed variable obtained from the particular feature processing transformation.