Patent ID: 11875232
Assignee: FAIR ISAAC CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer-implemented method for providing insights about a machine learning model, the method comprising:
during a first phase, using training data to train the machine learning model to learn patterns to determine whether data associated with an event provides an indication that the event belongs to a certain class from among a plurality of classes;
during a second phase, evaluating one or more outputs of the machine learning model to produce a data set pairing observed scores S and computing a set of predictive input variables Vi related to the input features of the machine learning model, the set not necessarily identical to the input features of the machine learning model;
constructing at least one data-driven estimator based on an explanatory statistic based on the predictive input variables Vi;
packaging the estimator with the machine learning model to provide a definition of explainability for a score generated by the machine learning model;
associating the definition of explainability with one or more non-linear features of the machine learning model;
selecting at least one candidate predictive feature or combination of candidate features with the explanatory statistic constructed to meet a first threshold value as providing the most relevant explanation;
generating one or more explanations based on the at least one selected candidate predictive feature or combination of candidate features that explain the score generated; and
performing one or more deduplication or explanatory elucidation procedures to enhance palatability and relevance of the one or more explanations.