Model Monitor

author: Troy William Raeder, University of Notre Dame
published: Dec. 20, 2008,   recorded: December 2008,   views: 3030


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Common practice in Machine Learning often implicitly assumes a stationary distribution, meaning that the distribution of a particular feature does not change over time. In practice, however, this assumption is often violated and real-world models have to be retrained as a result. It would be helpful, then, to be able to anticipate and plan for changes in distribution in order to avoid this retraining. Model Monitor is a Java toolkit that addresses this problem. It provides methods for detecting distribution shifts in data, comparing the performance of multiple classifiers under shifts in distribution, and evaluating the robustness of individual classifiers to distribution change. As such, it allows users to determine the best model (or models) for their data under a number of potential scenarios. Additionally, Model Monitor is fully integrated with the WEKA machine learning environment, so that a variety of commodity classifiers can be used if desired.

See Also:

Download slides icon Download slides: mloss08_raeder_mm_01.ppt (379.0 KB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: