Latest revision as of 21:08, 28 January 2021

Abstract

Machine learning tasks entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities in a pipeline produce erroneous or uninformative outputs, the pipeline may fail or produce incorrect results. Inferring the root cause of failures and unexpected behavior is challenging, usually requiring much human thought, and is both time-consuming and error-prone. We propose a new approach that makes use of iteration and provenance to automatically infer the root causes and derive succinct explanations of failures. Through a detailed experimental evaluation, we assess the cost, precision, and recall of our approach compared to the state of the art. Our source code and experimental data will be available for reproducibility and enhancement.

Comment: 10 page

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/2002.04640

• http://arxiv.org/pdf/2002.04640

• http://dl.acm.org/ft_gateway.cfm?id=3329489&ftid=2064725&dwn=1,

http://dx.doi.org/10.1145/3329486.3329489 under the license http://www.acm.org/publications/policies/copyright_policy#Background

• https://dblp.uni-trier.de/db/journals/corr/corr2002.html#abs-2002-04640,

https://arxiv.org/abs/2002.04640,

https://dl.acm.org/citation.cfm?id=3329489,

https://arxiv.org/pdf/2002.04640v1,

https://nyuscholars.nyu.edu/en/publications/debugging-machine-learning-pipelines,

https://doi.org/10.1145/3329486.3329489,

https://jp.arxiv.org/abs/2002.04640,

https://za.arxiv.org/abs/2002.04640,

https://uk.arxiv.org/abs/2002.04640,

https://it.arxiv.org/abs/2002.04640,

https://aps.arxiv.org/abs/2002.04640,

https://academic.microsoft.com/#/detail/2948038809