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US 2023/0144751 A1Published application
Managing machine learning features
AssigneeMeta Platforms, Inc.
InventorsHongzhong Jia, Jay Parikh
Life of this patent
Estimating the term…
Abstract
A machine learning model is trained. A feature importance metric is determined for each machine learning feature of a plurality of machine learning features of the machine learning model. Based on the feature importance metrics, one or more machine learning features of the plurality of machine learning features of the machine learning model are managed.
Claims20
A method, comprising:
- training a machine learning model;
- determining a feature importance metric for each machine learning feature of a plurality of machine learning features of the machine learning model;
- and
- based on the feature importance metrics of the plurality of machine learning features, managing one or more machine learning features of the plurality of machine learning features of the machine learning model;
- wherein determining the feature importance metric for a selected one of the machine learning features includes comparing a base performance of the machine learning model for a selected test dataset with a new performance of the machine learning model for a modified version of the selected test dataset of the selected machine learning feature.
2 more elements
A system, comprising:
- one or more processors configured to:
- train a machine learning model;
- determine a feature importance metric for each machine learning feature of a plurality of machine learning features of the machine learning model;
- and
- based on the feature importance metrics of the plurality of machine learning features, manage one or more machine learning features of the plurality of machine learning features of the machine learning model, wherein being configured to determine the feature importance metric for a selected one of the machine learning features includes being configured to compare a base performance of the machine learning model for a selected test dataset with a new performance of the machine learning model for a modified version of the selected test dataset of the selected machine learning feature;
- and
- a memory coupled to at least one of the one or more processors and configured to provide the at least one of the one or more processors with instructions.
4 more elements
A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:
- training a machine learning model;
- determining a feature importance metric for each machine learning feature of a plurality of machine learning features of the machine learning model;
- and
- based on the feature importance metrics of the plurality of machine learning features, managing one or more machine learning features of the plurality of machine learning features of the machine learning model;
- wherein determining the feature importance metric for a selected one of the machine learning features includes comparing a base performance of the machine learning model for a selected test dataset with a new performance of the machine learning model for a modified version of the selected test dataset of the selected machine learning feature.
2 more elements
Dependencies are inferred from the claim text.