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 an initial training set comprising a plurality of training data entries, wherein:
- (i) each training data entry of the plurality of training data entries is associated with a training data feature value for each initial training data feature of the plurality of initial training data features and a training target value for a training target feature, and (ii) each initial training data feature of the plurality of initial training data features is associated with a plurality of initial categorical levels;
- generating an adjusted training set based at least in part on the initial training set, wherein generating the adjusted training set comprises:
- generating a plurality of limited noise features based at least in part on the plurality of initial training data features, wherein generating the plurality of limited noise features comprises determining a limited noise subset of the plurality of initial training data features based at least in part on a per-feature mutual information measure for each initial training data feature of the plurality of initial training data features, and generating the adjusted training set based at least in part on the plurality of limited noise features;
- generating a categorical input machine learning model based at least in part on the adjusted training set;
- 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 an initial training set comprising a plurality of training data entries, wherein:
- (i) each training data entry of the plurality of training data entries is associated with a training data feature value for each initial training data feature of the plurality of initial training data features and a training target value for a training target feature, and (ii) each initial training data feature of the plurality of initial training data features is associated with a plurality of initial categorical levels;
- generate an adjusted training set based at least in part on the initial training set, wherein generating the adjusted training set comprises:
- generating a plurality of limited noise features based at least in part on the plurality of initial training data features, wherein generating the plurality of limited noise features comprises determining a limited noise subset of the plurality of initial training data features based at least in part on a per-feature mutual information measure for each initial training data feature of the plurality of initial training data features, and
- generating the adjusted training set based at least in part on the plurality of limited noise features;
- generate a categorical input machine learning model based at least in part on the adjusted training set;
- 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 an initial training set comprising a plurality of training data entries, wherein:
- (i) each training data entry of the plurality of training data entries is associated with a training data feature value for each initial training data feature of the plurality of initial training data features and a training target value for a training target feature, and (ii) each initial training data feature of the plurality of initial training data features is associated with a plurality of initial categorical levels;
- generate an adjusted training set based at least in part on the initial training set, wherein generating the adjusted training set comprises:
- generating a plurality of limited noise features based at least in part on the plurality of initial training data features, wherein generating the plurality of limited noise features comprises determining a limited noise subset of the plurality of initial training data features based at least in part on a per-feature mutual information measure for each initial training data feature of the plurality of initial training data features, and
- generating the adjusted training set based at least in part on the plurality of limited noise features;
- generate a categorical input machine learning model based at least in part on the adjusted training set;
- 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.