Model interpretation

A system, comprising:

• a processor configured to:
• receive an indication of a selection of an entry associated with a linear surrogate machine learning model, wherein the linear surrogate machine learning model approximates an output associated with a machine learning model;
• in response to the received indication of the selection of the entry associated with the linear surrogate machine learning model, dynamically update one or more interpretation views associated with one or more other surrogate machine learning models based on the selected entry, wherein to dynamically update one or more interpretation views associated with the one or more other surrogate machine learning models, the processor is configured to update a first non-linear surrogate machine learning model comprising a feature importance model that includes corresponding global feature importance values and corresponding local importance feature values for a plurality of features;
• determine whether a local feature importance value associated with the selected entry correlates with a global feature importance value associated with the selected entry;
• flag the selected entry in response to determining that the local feature importance value associated with the selected entry does not correlate with the global feature importance value associated with the selected entry, wherein the local feature importance value associated with the selected entry does not correlate with the global feature importance value associated with the selected entry in the event a difference between a first feature local feature importance value of a first feature of the plurality of features and a first feature global feature importance value of the first feature is greater than a threshold value;
• and
• in response to a threshold number of entries associated with the linear surrogate machine learning model being flagged, retrain the first non-linear surrogate machine learning model that includes the feature importance model;
• and
• a memory coupled to the processor and configured to provide the processor with instructions.

A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:

• receiving an indication of a selection of an entry associated with a linear surrogate machine learning model, wherein the linear surrogate machine learning model approximates an output associated with a machine learning model;
• in response to the received indication of the selection of the entry associated with the linear surrogate machine learning model, dynamically updating one or more interpretation views associated with one or more other surrogate machine learning models based on the selected entry, wherein dynamically updating one or more interpretation views associated with the one or more other surrogate machine learning models includes updating a first non-linear surrogate machine learning model comprising a feature importance model that includes corresponding global feature importance values and corresponding local importance feature values for a plurality of features;
• determining whether a local feature importance value associated with the selected entry correlates with a global feature importance value associated with the selected entry;
• flagging the selected entry in response to determining that the local feature importance value associated with the selected entry does not correlate with the global feature importance value associated with the selected entry, wherein the local feature importance value associated with the selected entry does not correlate with the global feature importance value associated with the selected entry in the event a difference between a first feature local feature importance value of a first feature of the plurality of features and a first feature global feature importance value of the first feature is greater than a threshold value;
• and
• in response to a threshold number of entries associated with the linear surrogate machine learning model being flagged, retraining the first non-linear surrogate machine learning model that includes the feature importance model.