Predictive models having decomposable hierarchical layers configured to generate interpretable results

A system comprising:

• at least one memory storing computer-executable instructions;
• and
• at least one processor configured to access the at least one memory and perform a method for providing interpretable predictions from a machine learning model, the method comprising:
• receiving, by a processor of a computing device, a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y);
• building, by the processor, an interpretability model corresponding to the predictive models, by assigning an interpretability to each prediction Yi of the predictive model based on the hierarchical structure, wherein assigning the interpretability comprises:
• decomposing, by the processor, the set of features (X) of the input data into a plurality of partitions Xj using the hierarchical structure, wherein X=U1NXj, N being the number of partitions;
• decomposing, by the processor, each partition from the partitions into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained;
• and
• computing, by the processor, a score for each partition as a function of the predicted scores of the sub-partitions of said each partition, wherein the predicted scores represent interactions between the sub-partitions;
• and
• outputting, by the processor, an interpretation of a prediction provided by the predictive model based on the interpretability model, wherein the interpretation represents a weightage of the one or more features used by the predictive model to generate the prediction.

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to perform a method comprising:

• receiving, by a processor of a computing device, a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y);
• building, by the processor, an interpretability model corresponding to the predictive models, by assigning an interpretability to each prediction Yi of the predictive model based on the hierarchical structure, wherein assigning the interpretability comprises:
• decomposing, by the processor, the set of features (X) of the input data into a plurality of partitions Xj using the hierarchical structure, wherein X=U1NXj, N being the number of partitions;
• decomposing, by the processor, each partition from the partitions into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained;
• and
• computing, by the processor, a score for each partition as a function of the predicted scores of the sub-partitions of said each partition, wherein the predicted scores represent interactions between the sub-partitions;
• and
• outputting, by the processor, an interpretation of a prediction provided by the predictive model based on the interpretability model, wherein the interpretation represents a weightage of the one or more features used by the predictive model to generate the prediction.