System and method for efficient analyzing and comparing slice-based machine learn models

Abstract

A computer-implemented method for a machine-learning network that includes receiving an input dataset, sending the input dataset to a first machine-learning model to output predictions associated with the input data, identifying one or more slices associated with the input dataset and a first machine learning model in a first iteration, wherein each of the one or more slices include input data from the input dataset and common attributes associated with each slice; upon selecting one or more slices of the input dataset, training a shallow regressor model configured to predict residuals associated with the model, create a representation associated with a ground-truth label and a second representation associated with a model prediction associated with each sample associated with each of the one or more slices, determine residuals associated with every prediction of the first machine learning model, training the shallow regressor to compute one or more predicted residuals of the selected slices, generate an optimized model the predicted residuals, determine a modified accuracy of optimized predictions from the optimized model on each of the one or more slices of the input dataset, determine a modified effect of each of the one or more slices by utilizing a difference between the modified accuracy and an original accuracy associated with the first machine learning model, and output the modified effect to a graphical interface.