MATEC Web Conf.
Volume 232, 20182018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|Number of page(s)||7|
|Section||Network Security System, Neural Network and Data Information|
|Published online||19 November 2018|
The Application of Tree-based model to Unbalanced German Credit Data Analysis
Allendale Columbia School, 519 Allens Creek Road, Rochester 14618, NY, USA
With the development of financial consumption, demand for credit has soared. Since the bank has detailed client data, it is important to build effective models to distinguish between high-risk groups and low-risk groups. However, traditional credit evaluation methods including expert opinion, credit rating and credit scoring are very subjective and inaccurate. Moreover, the data are highly unbalanced since the number of high-risk groups is significantly less than that of low-risk groups. Progress in machine learning makes it possible to conduct accurate credit analysis. The tree-based machine learning models are particularly suitable for the unbalanced credit data by weighting the credit individuals. We apply a series of tree-based machine learning models to analyze the German Credit Data from the UCI Repository of Machine Learning Databases.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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