Categorical input machine learning models
AssigneeOptum Services (Ireland) Limited
InventorsLorcan B. Mac Manus, Peter Cogan, Conor Breen
Life of this patent
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Abstract
There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.
Claims20
A computer-implemented method for performing predictive data analysis using categorical input data, the computer-implemented method comprising:
- identifying a prediction input data entry;
- identifying a categorical input machine learning model, wherein the categorical input machine learning model defines a plurality of training data feature value arrangements;
- determining a matching subset of the plurality of training data feature value arrangements that correspond to the prediction input data entry;
- generating an inferred prediction for the prediction input data entry based at least in part on the matching subset;
- generating explanatory metadata for the inferred prediction based at least in part on the matching subset;
- and
- performing one or more prediction-based actions based at least in part on the inferred prediction and the explanatory metadata.
An apparatus for performing predictive data analysis using categorical input data, the apparatus comprising at least one processor and at least one memory including program code, the program code configured to, with the processor, cause the apparatus to at least:
- identify a prediction input data entry;
- identify a categorical input machine learning model, wherein the categorical input machine learning model defines a plurality of training data feature value arrangements;
- determine a matching subset of the plurality of training data feature value arrangements that correspond to the prediction input data entry;
- generating an inferred prediction for the prediction input data entry based at least in part on the matching subset;
- generate explanatory metadata for the inferred prediction based at least in part on the matching subset;
- and
- perform one or more prediction-based actions based at least in part on the inferred prediction and the explanatory metadata.
A computer program product for performing predictive data analysis using categorical input data, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:
- identify a prediction input data entry;
- identify a categorical input machine learning model, wherein the categorical input machine learning model defines a plurality of training data feature value arrangements;
- determine a matching subset of the plurality of training data feature value arrangements that correspond to the prediction input data entry;
- generating an inferred prediction for the prediction input data entry based at least in part on the matching subset;
- generate explanatory metadata for the inferred prediction based at least in part on the matching subset;
- and
- perform one or more prediction-based actions based at least in part on the inferred prediction and the explanatory metadata.
Dependencies are inferred from the claim text.