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Comprehensible Explanation of Predictive Models

Comprehensible Explanation of Predictive Models

Marko Robnik-Šikonja
Copyright: © 2019 |Pages: 12
ISBN13: 9781522573623|ISBN10: 1522573623|EISBN13: 9781522573630
DOI: 10.4018/978-1-5225-7362-3.ch046
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MLA

Robnik-Šikonja, Marko. "Comprehensible Explanation of Predictive Models." Advanced Methodologies and Technologies in Business Operations and Management, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2019, pp. 612-623. https://doi.org/10.4018/978-1-5225-7362-3.ch046

APA

Robnik-Šikonja, M. (2019). Comprehensible Explanation of Predictive Models. In M. Khosrow-Pour, D.B.A. (Ed.), Advanced Methodologies and Technologies in Business Operations and Management (pp. 612-623). IGI Global. https://doi.org/10.4018/978-1-5225-7362-3.ch046

Chicago

Robnik-Šikonja, Marko. "Comprehensible Explanation of Predictive Models." In Advanced Methodologies and Technologies in Business Operations and Management, edited by Mehdi Khosrow-Pour, D.B.A., 612-623. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7362-3.ch046

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Abstract

The most successful prediction models (e.g., SVM, neural networks, or boosting) unfortunately do not provide explanations of their predictions. In many important applications of machine learning, the comprehension of the decision process is of utmost importance and dominates the classification accuracy (e.g., in business and medicine). This chapter introduces general explanation methods that are independent of the prediction model and can be used with all classification models that output probabilities. It explains how the methods work and graphically explains models' decisions for new unlabeled cases. The approach is put in the context of applications from medicine, business, and macro-economy.

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