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 training set comprising a plurality of training data entries, wherein each training data entry of the plurality of training data entries is associated with a training data feature value for each training data feature of a plurality of training data features and a training target value for a training target feature;
- for each training data entry of the plurality of training data entries:
- determining a plurality of training data feature value arrangements, wherein each training data feature value arrangement of the plurality of training data feature value arrangements comprises a defined value for each training data feature in a related subset of the plurality of training data features,
- determining a target distribution with respect to the training target feature based at least in part on each training data feature value arrangement of the plurality of training data feature value arrangements, and
- determining a mean-deviating subset of the plurality of training data feature value arrangements based at least in part on the target distribution;
- generating a categorical input machine learning model based at least in part on each mean-deviating subset for a training data entry of the plurality of training data entries;
- and
- enabling access to the categorical input machine learning model to generate one or more inferred predictions based at least in part on a prediction input data entry and to perform one or more prediction-based actions based at least in part on the one or more inferred predictions.
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 training set comprising a plurality of training data entries, wherein each training data entry of the plurality of training data entries is associated with a training data feature value for each training data feature of a plurality of training data features and a training target value for a training target feature;
- for each training data entry of the plurality of training data entries:
- determine a plurality of training data feature value arrangements, wherein each training data feature value arrangement of the plurality of training data feature value arrangements comprises a defined value for each training data feature in a related subset of the plurality of training data features,
- determine a target distribution with respect to the training target feature based at least in part on each training data feature value arrangement of the plurality of training data feature value arrangements, and
- determine a mean-deviating subset of the plurality of training data feature value arrangements based at least in part on the target distribution;
- generate a categorical input machine learning model based at least in part on each mean-deviating subset for a training data entry of the plurality of training data entries;
- and
- enable access to the categorical input machine learning model to generate one or more inferred predictions based at least in part on a prediction input data entry and to perform one or more prediction-based actions based at least in part on the one or more inferred predictions.
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 training set comprising a plurality of training data entries, wherein each training data entry of the plurality of training data entries is associated with a training data feature value for each training data feature of a plurality of training data features and a training target value for a training target feature;
- for each training data entry of the plurality of training data entries:
- determine a plurality of training data feature value arrangements, wherein each training data feature value arrangement of the plurality of training data feature value arrangements comprises a defined value for each training data feature in a related subset of the plurality of training data features,
- determine a target distribution with respect to the training target feature based at least in part on each training data feature value arrangement of the plurality of training data feature value arrangements, and
- determine a mean-deviating subset of the plurality of training data feature value arrangements based at least in part on the target distribution;
- generate a categorical input machine learning model based at least in part on each mean-deviating subset for a training data entry of the plurality of training data entries;
- and
- enable access to the categorical input machine learning model to generate one or more inferred predictions based at least in part on a prediction input data entry and to perform one or more prediction-based actions based at least in part on the one or more inferred predictions.
Dependencies are inferred from the claim text.