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2018 | OriginalPaper | Chapter

Decision Trees as Interpretable Bank Credit Scoring Models

Authors : Andrzej Szwabe, Pawel Misiorek

Published in: Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety

Publisher: Springer International Publishing

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Abstract

We evaluate several approaches to classification of loan applications that provide their final results in the form of a single decision tree, i.e., in the form widely regarded as interpretable by humans. We apply state-of-the-art credit scoring-oriented classification algorithms, such as logistic regression, gradient boosting decision trees and random forests, as components of the proposed algorithms of decision tree building. We use four real-world loan default prediction data sets of different sizes. We evaluate the proposed methods using the area under the receiver operating characteristic curve (AUC) but we also measure the models’ interpretability. We verify the significance of differences between AUC values observed when using the compared techniques by measuring Friedman’s statistic and performing Nemenyi’s post-hoc test.

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Title: Decision Trees as Interpretable Bank Credit Scoring Models
Authors: Andrzej Szwabe
Pawel Misiorek
Publisher: Springer International Publishing
Book: Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety
Print ISBN: 978-3-319-99986-9

Electronic ISBN: 978-3-319-99987-6

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-99987-6_16

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