Abstract: A model evaluation system evaluates the effect of a feature value at a particular time in a time-series data record on predictions made by a time-series model. The time-series model may make predictions with black-box parameters that can impede explainability of the relationship between predictions for a data record and the values of the data record. To determine the relative importance of a feature occurring at a time and evaluated at an evaluation time, the model predictions are determined on the unmasked data record at the evaluation time and on the data record with feature values masked within a window between the time and the evaluation time, permitting comparison of the evaluation with the features and without the features. In addition, the contribution at the initial time in the window may be determined by comparing the score with another score determined by masking the values except for the initial time.

Abstract: The disclosed embodiments include computer-implemented systems and methods that predicts targeted, agency-specific recovery events using a trained machine-learning or artificial-intelligence processes. For example, an apparatus may generate an input dataset based on elements of interaction data associated with an occurrence of a first event. Based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate elements of output data indicative of an expected occurrence of a corresponding one of a plurality of targeted second events involving each of a plurality of candidate event assignments during a future temporal interval. The apparatus may transmit at least a portion of the generated output data to a computing system via the communications interface, the computing system may perform operations that assign the first event to a corresponding one of the candidate event assignments based on the elements of output data.