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A Framework for Quality Assurance of Machine Learning Applications

Christian Murphy; Gail E. Kaiser; Marta Arias; Columbia University. Computer Science

Title:
A Framework for Quality Assurance of Machine Learning Applications
Author(s):
Murphy, Christian; Kaiser, Gail E.; Arias, Marta; Columbia University. Computer Science
Date:
Type:
Technical reports
Department:
Computer Science
Permanent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-034-06
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:
377
Metadata:
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