A Method of Prediction Error Decomposition to Accelerate MLOps

Abstract

In order to continuously operate machine learning models, it is necessary to find the causes of prediction errors and take appropriate measures. At this time, it is possible to estimate the effectiveness of measures for each cause by quantitatively evaluating the impact of each cause on the prediction error. This study proposes a method to decompose the prediction errors occurring in operation into contributions from multiple causes. We verified the usefulness of the proposed method through experiments.