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• W2068840577 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." @default.
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• W2068840577 date "2009-08-20" @default.
• W2068840577 modified "2023-09-25" @default.
• W2068840577 title "Explanation and reliability of prediction models: the case of breast cancer recurrence" @default.
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• W2068840577 doi "https://doi.org/10.1007/s10115-009-0244-9" @default.
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