Gestalt: integrated support for implementation and analysis in machine learning

Authors:
Kayur Patel;Naomi Bancroft;Steven M. Drucker;James Fogarty;Andrew J. Ko;James Landay
Affiliations:
University of Washington, Seattle, WA, USA;University of Washington, Seattle, WA, USA;Microsoft Research, Seattle, WA, USA;University of Washington, Seattle, WA, USA;University of Washington, Seattle, WA, USA;University of Washington, Seattle, WA, USA
Venue:
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Year:
2010

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Abstract

We present Gestalt, a development environment designed to support the process of applying machine learning. While traditional programming environments focus on source code, we explicitly support both code and data. Gestalt allows developers to implement a classification pipeline, analyze data as it moves through that pipeline, and easily transition between implementation and analysis. An experiment shows this significantly improves the ability of developers to find and fix bugs in machine learning systems. Our discussion of Gestalt and our experimental observations provide new insight into general-purpose support for the machine learning process.