Concept drift is a major issue that greatly affects the accuracy and reliability of many real-world applications of machine learning. We argue that to tackle concept drift it is important to develop the capacity to describe and analyze it. We propose tools for this purpose, arguing for the importance of quantitative descriptions of drift in marginal distributions. We present quantitative drift analysis techniques along with methods for communicating their results. We demonstrate their effectiveness by application to three real-world learning tasks.
@article{arxiv.1704.00362,
title = {Understanding Concept Drift},
author = {Geoffrey I. Webb and Loong Kuan Lee and François Petitjean and Bart Goethals},
journal= {arXiv preprint arXiv:1704.00362},
year = {2017}
}