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A Framework for Quality Assurance of Machine Learning Applications

Christian Murphy; Gail E. Kaiser; Marta Arias

Title:
A Framework for Quality Assurance of Machine Learning Applications
Author(s):
Murphy, Christian
Kaiser, Gail E.
Arias, Marta
Date:
Type:
Technical reports
Department:
Computer Science
Permanent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-034-06
Publisher:
Department of Computer Science, Columbia University
Publisher Location:
New York
Abstract:
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).
Subject(s):
Computer science
Item views:
366
Metadata:
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