Model Monitor (M2): Evaluating, Comparing, and Monitoring Models

Troy Raeder; Nitesh V. Chawla

This paper presents Model Monitor (M²), a Java toolkit for robustly evaluating machine learning algorithms in the presence of changing data distributions. M² provides a simple and intuitive framework in which users can evaluate classifiers under hypothesized shifts in distribution and therefore determine the best model (or models) for their data under a number of potential scenarios. Additionally, M² is fully integrated with the WEKA machine learning environment, so that a variety of commodity classifiers can be used if desired.

Model Monitor (M2): Evaluating, Comparing, and Monitoring Models

Abstract