Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Comprehensible Explanation of Predictive Models

Comprehensible Explanation of Predictive Models
View Sample PDF
Author(s): Marko Robnik-Šikonja (University of Ljubljana, Slovenia)
Copyright: 2018
Pages: 10
Source title: Encyclopedia of Information Science and Technology, Fourth Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-5225-2255-3.ch181


View Comprehensible Explanation of Predictive Models on the publisher's website for pricing and purchasing information.


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 uttermost 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 unlabelled cases. The approach is put in the context of applications from medicine, business and macro economy.

Related Content

Yair Wiseman. © 2021. 11 pages.
Mário Pereira Véstias. © 2021. 15 pages.
Mahfuzulhoq Chowdhury, Martin Maier. © 2021. 15 pages.
Gen'ichi Yasuda. © 2021. 12 pages.
Alba J. Jerónimo, María P. Barrera, Manuel F. Caro, Adán A. Gómez. © 2021. 19 pages.
Gregor Donaj, Mirjam Sepesy Maučec. © 2021. 14 pages.
Udit Singhania, B. K. Tripathy. © 2021. 11 pages.
Body Bottom