Abstract: An example operation may include one or more of receiving a loan application of a user, extracting a plurality of personal attributes about the user from the loan application, querying a machine learning model via an application programming interface (API) based on the plurality of attributes about the user to identify one or more rules for auto-adjudicating the loan application, determining whether or not to approve the loan application based on the one or more rules identified via the machine learning model, and transmitting notice of the determination to a device associated with the user.

Abstract: An example operation may include one or more of assigning different criteria to a plurality of nodes of a decision tree model, respectively, iteratively executing the decision tree model on a plurality of training data which causes the plurality of training data to be assigned to the plurality of nodes of the decision tree model based on the different criteria assigned to the plurality of nodes, identifying nodes among the plurality of nodes within the decision tree model which have a purity above a predetermined purity threshold, generating a set of rules based on the identified nodes which have the purity above the predetermined purity threshold, and embedding the set of rules within the decision tree model and storing the decision tree model within a storage device.

Abstract: Computational systems and methods are provided to automatically assess residual characteristics of an existing machine learning model to identify and determine suboptimal pockets and augmentation strategies. A computing system, device and method for optimizing a machine learning model for performing predictions is provided. The computing device performs sub-optimal pocket identification on an existing machine learning algorithm by residual analysis to calculate an error. The computing device utilizes the residual as a target for an ensemble tree model and automatically generates a set of interpretable rules from the tree based ensemble model that contribute to the suboptimal pockets. The rules indicating relationships between features and interactions as well as values for the sub-optimal pockets. The computing device determines optimizations for improving the machine learning model based on the interpretable computer-implemented rules.