System for automatic error estimate correction for a machine learning model

Abstract

A computing device computes curve descriptive values to correct an error estimate of a prediction. A predefined number of times, an input dataset is split into a training dataset and a validation dataset, a predictive model and a domain model are trained, the trained predictive model and the trained domain model are validated, a predictive error value, a residual value, and a domain error value are computed, and each value is stored in output data. A domain threshold value is computed from the stored domain error values. Each predictive error value and each residual value stored in the output data is stored in in-domain output data when a respective domain error value is less than or equal to the computed domain threshold value. Curve descriptive values are computed to describe a relationship between the residual values as a function of the prediction error values stored in the in-domain output data.