Model Monitor (m2): Evaluating, Comparing, and Monitoring Models
Abstract
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.
Cite
Text
Raeder and Chawla. "Model Monitor (m2): Evaluating, Comparing, and Monitoring Models." Machine Learning Open Source Software, 2009.Markdown
[Raeder and Chawla. "Model Monitor (m2): Evaluating, Comparing, and Monitoring Models." Machine Learning Open Source Software, 2009.](https://mlanthology.org/mloss/2009/raeder2009jmlr-model/)BibTeX
@article{raeder2009jmlr-model,
title = {{Model Monitor (m2): Evaluating, Comparing, and Monitoring Models}},
author = {Raeder, Troy and Chawla, Nitesh V.},
journal = {Machine Learning Open Source Software},
year = {2009},
pages = {1387-1390},
volume = {10},
url = {https://mlanthology.org/mloss/2009/raeder2009jmlr-model/}
}