Decision Trees as Interpretable Bank Credit Scoring Models
[ 1 ] Instytut Automatyki, Robotyki i Inżynierii Informatycznej, Wydział Elektryczny, Politechnika Poznańska | [ P ] employee
2018
chapter in monograph / paper
english
- data extraction and integration
- credit scoring
- expert system and artificial intelligence
- big data
- data processing performance
EN 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.
207 - 219
20
WoS (15)