Training data fidelity for machine learning applications through intelligent merger of curated auxiliary data

A non-transitory computer-readable medium storing instructions which, when executed by a processing system including at least one processor, cause the processing system to perform operations, the operations comprising:

• identifying a target performance metric of a machine learning algorithm, wherein the target performance metric comprises a performance metric that is to be improved;
• obtaining a set of auxiliary data from a plurality of auxiliary data sources, wherein the plurality of auxiliary data sources is separate from a source of a training data set that is used to train the machine learning algorithm;
• selecting a candidate attribute type from the set of auxiliary data;
• identifying a quality metric for the candidate attribute type;
• calculating a change in the target performance metric of the machine learning algorithm when data values associated with the candidate attribute type are included in the training data set;
• determining that a tradeoff between the target performance metric and the quality metric of the candidate attribute type is satisfied by inclusion of the data values associated with the candidate attribute type in the training data set;
• and
• training the machine learning algorithm using the training data set augmented with the data values associated with the candidate attribute type.

A device comprising:

• a processing system including at least one processor;
• and
• a non-transitory computer-readable medium storing instructions which, when executed by the processing system, cause the processing system to perform operations, the operations comprising:
• identifying a target performance metric of a machine learning algorithm, wherein the target performance metric comprises a performance metric that is to be improved;
• obtaining a set of auxiliary data from a plurality of auxiliary data sources, wherein the plurality of auxiliary data sources is separate from a source of a training data set that is used to train the machine learning algorithm;
• selecting a candidate attribute type from the set of auxiliary data;
• identifying a quality metric for the candidate attribute type;
• calculating a change in the target performance metric of the machine learning algorithm when data values associated with the candidate attribute type are included in the training data set;
• determining that a tradeoff between the target performance metric and the quality metric of the candidate attribute type is satisfied by inclusion of the data values associated with the candidate attribute type in the training data set;
• and
• training the machine learning algorithm using the training data set augmented with the data values associated with the candidate attribute type.