Automated generation of explainable machine learning

A computing system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable storage devices storing computer-executable instructions, the instructions operable to cause the one or more processors to perform a machine learning pipeline process comprising:
• receiving feature datasets from a database;
• obtaining a set of models, each model being selected to be compatible with at least one of the feature datasets;
• training each model of the set of models with the at least one feature dataset to create a set of trained models;
• generating an accuracy value and explainability properties for each model of the set of the trained models;
• and
• selecting an explainable model as a recommended model from the set of the trained models based on the accuracy value and the explainability properties.

A computing system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable storage devices storing computer-executable instructions, the instructions operable to cause the one or more processors to perform a machine learning pipeline process comprising:
• receiving feature datasets from a database;
• obtaining a set of models, each model being selected to be compatible with at least one of the feature datasets;
• training each model of the set of models with the at least one feature dataset to create a set of trained models;
• generating an accuracy value and explainability properties for each model of the set of the trained models;
• ranking the set of the trained models based on the accuracy value and the explainability properties of each model in the set of trained models;
• and
• selecting an explainable model as a recommended model from the set of the trained models based on the accuracy value and the explainability properties of the ranked set of trained models.