Understanding Concept Drift

TL;DR

This paper emphasizes the importance of quantitatively analyzing concept drift in machine learning, introducing new tools and methods to describe and communicate drift, demonstrated through real-world applications.

Contribution

It proposes novel quantitative techniques for analyzing and communicating concept drift, enhancing understanding and management of drift in real-world machine learning tasks.

Findings

Effective drift analysis methods demonstrated on three real-world tasks

Quantitative descriptions improve understanding of concept drift

Tools facilitate better communication of drift analysis results

Abstract

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.