Explanation and reliability of prediction models: the case of breast cancer recurrence

Authors:
Erik Štrumbelj;Zoran Bosnić;Igor Kononenko;Branko Zakotnik;Cvetka Grašič Kuhar
Affiliations:
University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia;University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia;University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia;Institute of Oncology, Ljubljana, Slovenia;Institute of Oncology, Ljubljana, Slovenia
Venue:
Knowledge and Information Systems
Year:
2010

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Abstract

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.