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.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Moreno-Torres, Jose G.; Raeder, Troy; Alaiz-Rodríguez, Rocío; Chawla, Nitesh V.; Herrera, Francisco: A unifying view on dataset shift in classification (2012)
- Raeder, Troy; Chawla, Nitesh V.: Model monitor $(M^2)$: evaluating, comparing, and monitoring models (2009)