Abstract: Systems, apparatuses, methods, and computer program products are disclosed for training a GAMI-Tree model. An example method includes initializing an iterative prediction model and performing a required number of model training iterations. For each model training iteration, the method further includes (i) performing a required number of main-effect gradient boosting iterations of a main-effect gradient boosting routine, (ii) generating a plurality of qualified input feature pairs, and (iii) performing a required number of interaction-effect gradient boosting iterations of a interaction-effect gradient boosting routine. The method further includes generating the GAMI-Tree model based on the iterative prediction model generated by a final interaction-effect gradient boosting iteration of a final model training iteration.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for generating a predictive contribution report for an attribute using machine learning techniques. An example method includes generating an entity score for an entity using a predictive analysis machine learning model. The method further includes, in an instance the entity score fails to satisfy a determination decision threshold, selecting a reference entity from a plurality of candidate reference entities and determining a plurality of per-candidate feature contribution scores using a predictive analysis machine learning model. The method further includes generating a predictive contribution report, where the predictive contribution report includes an indication that the entity does not satisfy the determination decision threshold, and an indication of one or more candidate features associated with largest contributions to the entity score.

Abstract: Computing systems and technical methods that transform data structures and pierce opacity difficulties associated with complex machine learning modules are disclosed. Advances include a framework and techniques that include: i) global diagnostics; ii) locally interpretable models LIME-SUP-R and LIME-SUP-D; and iii) explainable neural networks. Advances also include integrating LIME-SUP-R and LIME-SUP-D approaches that create a transformed data structure and replicated modeling over local and global effects and that yield high interpretability along with high accuracy of the replicated complex machine learning modules that make up a machine learning application.

Abstract: Computing systems and technical methods that transform data structures and pierce opacity difficulties associated with complex machine learning modules are disclosed. Advances include a framework and techniques that include: i) global diagnostics; ii) locally interpretable models LIME-SUP-R and LIME-SUP-D; and iii) explainable neural networks. Advances also include integrating LIME-SUP-R and LIME-SUP-D approaches that create a transformed data structure and replicated modeling over local and global effects and that yield high interpretability along with high accuracy of the replicated complex machine learning modules that make up a machine learning application.