System and Method for Feature-Based Machine Learning (ML) Model Prediction

A computer-based system for feature-based machine learning (ML) model prediction, the computer-based system comprising:

• at least one processor;
• and
• at least one memory, the at least one having encoded thereon a sequence of instructions which, when loaded and executed by the at least one processor, causes the computer-based system to:
• use an imputation method to produce posterior distributions of unprovided features of a set of retrospective features, the posterior distributions produced based on the set of retrospective features and provided features of the set of retrospective features;
• employ an ML model to produce a threshold and a risk score distribution of a prediction of an event, the producing based on the posterior distributions produced by the imputation method used and the provided features, the ML model trained on the set of retrospective features;
• select at least one unprovided feature from a partial set of the unprovided features to improve predictive accuracy of the ML model iteratively, selection of the at least one unprovided feature being based on the threshold and the risk score distribution of the prediction of the event;
• and
• output a representation of the at least one unprovided feature selected toward approximating a full-feature-capacity (FFC) prediction with a partial set of the retrospective features, the FFC prediction based on the set of retrospective features in its entirety, the representation output causing the at least one unprovided feature to be provided for a subsequent iteration, the partial set of the retrospective features including the provided features supplemented by the at least one unprovided feature selected and provided at the subsequent iteration.

A non-transitory computer-readable medium having encoded thereon a sequence of instructions which, when loaded and executed by at least one processor, causes the at least one processor to:

• use an imputation method to produce posterior distributions of unprovided features of a set of retrospective features, the posterior distributions produced based on the set of retrospective features and provided features of the set of retrospective features;
• employ an ML model to produce a threshold and a risk score distribution of a prediction of an event, the producing based on the posterior distributions produced by the imputation method used and the provided features, the ML model trained on the set of retrospective features;
• select at least one unprovided feature from a partial set of the unprovided features to improve predictive accuracy of the ML model iteratively, selection of the at least one unprovided feature being based on the threshold and the risk score distribution of the prediction of the event;
• and
• output a representation of the at least one unprovided feature selected toward approximating a full-feature-capacity (FFC) prediction with a partial set of the retrospective features, the FFC prediction based on the set of retrospective features in its entirety, the representation output causing the at least one unprovided feature to be provided for a subsequent iteration, the partial set of the retrospective features including the provided features supplemented by the at least one unprovided feature selected and provided at the subsequent iteration.