Journal
KNOWLEDGE AND INFORMATION SYSTEMS
Volume 24, Issue 2, Pages 305-324Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s10115-009-0244-9
Keywords
Data mining; Machine learning; Breast cancer; Classification explanation; Prediction reliability
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In this paper, we describe the first practical application of two methods, which bridge the gap between the non-expert user and machine learning models. The first is a method for explaining classifiers' predictions, which provides the user with additional information about the decision-making process of a classifier. The second is a reliability estimation methodology for regression predictions, which helps the users to decide to what extent to trust a particular prediction. Both methods are successfully applied to a novel breast cancer recurrence prediction data set and the results are evaluated by expert oncologists.
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