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

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. [abs] [ pdf ][ bib ] [ code ] © JMLR 2009. (edit, beta)