A Framework for Quality Assurance of Machine Learning Applications
- A Framework for Quality Assurance of Machine Learning Applications
- Murphy, Christian
Kaiser, Gail E.
- Technical reports
- Computer Science
Center for Computational Learning Systems
- Persistent URL:
- Columbia University Computer Science Technical Reports
- Part Number:
- Department of Computer Science, Columbia University
- Publisher Location:
- New York
- Some machine learning applications are intended to learn properties of data sets where the correct answers are not already known to human users. It is challenging to test and debug such ML software, because there is no reliable test oracle. We describe a framework and collection of tools aimed to assist with this problem. We present our findings from using the testing framework with three implementations of an ML ranking algorithm (all of which had bugs).
- Computer science
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- Suggested Citation:
- Christian Murphy, Gail E. Kaiser, Marta Arias, 2006, A Framework for Quality Assurance of Machine Learning Applications, Columbia University Academic Commons, http://hdl.handle.net/10022/AC:P:29471.