CHAINED INFLUENCE SCORES FOR IMPROVING SYNTHETIC DATA GENERATION

摘要

The embodiments described herein combine a number of mathematical techniques to address the problem of efficiently assessing the quality of predictions by machine learning models or explaining said predictions to a user. Influence functions are used to estimate the influence of training data points on a particular prediction made by a model in order to help explain why that prediction was justified. Through the use of influence functions, repeated retraining of the model is avoided, thereby providing a more computationally efficient means of assessing the quality of the predictions. In addition, a novel quality metric is proposed for effectively quantifying the quality of a particular prediction.