Reference Information:
Title: Gestalt: Integrated Support for Implementation and Analysis in Machine Learning
Authors: Kayur Patel, Naomi Bancroft, Steven M. Drucker, James Fogarty, Andrew J. Ko, James A. Landay
Conference: UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
This paper talks about Gestalt, a development environment designed to support the process of applying machine learning. While traditional programming environments focus on source code, Gestalt explicitly supports 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 showed that this significantly improved the ability of developers to find and fix bugs in machine learning systems. Gestalt provides new insight into general-purpose support for the machine learning process. Gestalt allows the developers to graphically implement a classification pipeline and analyze the data. The developer can graphically switch back and forth between two operations. The way this was implemented was using add-on widgets to existing IDEs like Eclipse and MS Visual Studio. They added widgets to perform tasks like parse data, train and test models.
Discussion:
I can understand how helpful this software will be in developing machine learning algorithms. It's extremely difficult to visualize the working from the lines of code that you've written and finding bugs is even harder. This IDE plugin will immensely help developers like you and me to visualize the data, spot the discrepancies and fix the bugs. Similar tools will save a lot of time spent in the debugging process and increase efficiency.
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