Affiliation:
1. University of Washington, Seattle, WA, USA
Abstract
Progress indicators are desirable for machine learning model building that often takes a long time, by continuously estimating the remaining model building time and the portion of model building work that has been finished. Recently, we proposed a high-level framework using system approaches to support nontrivial progress indicators for machine learning model building, but offered no detailed implementation technique. It remains to be seen whether it is feasible to provide such progress indicators. In this paper, we fill this gap and give the first demonstration that offering such progress indicators is viable. We describe detailed progress indicator implementation techniques for three major, supervised machine learning algorithms. We report an implementation of these techniques in Weka.
Publisher
Association for Computing Machinery (ACM)
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