The Building of Credit Card Scoring Via Logistic Regression Model Combined with Decision Tree

碩士 === 輔仁大學 === 應用統計學研究所 === 96 === The objective of this study is to build a credit scoring system to guard against management risk for the credit-issue financial corporations. A pragmatic devise merging the decision tree CHAID techniques with logistic regression is proposed and applied to the mod...

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Main Authors:	Hsu Jung-Chieh, 許榮傑
Other Authors:	Rwei-Ju CHUANG
Format:	Others
Language:	zh-TW
Published:	2008
Online Access:	http://ndltd.ncl.edu.tw/handle/93622354485551178617

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spelling	ndltd-TW-096FJU005060162015-11-30T04:02:17Z http://ndltd.ncl.edu.tw/handle/93622354485551178617 The Building of Credit Card Scoring Via Logistic Regression Model Combined with Decision Tree 應用羅吉斯迴歸模型與決策樹建置信用評分卡 Hsu Jung-Chieh 許榮傑碩士輔仁大學應用統計學研究所 96 The objective of this study is to build a credit scoring system to guard against management risk for the credit-issue financial corporations. A pragmatic devise merging the decision tree CHAID techniques with logistic regression is proposed and applied to the model building. This twofold approach uses the classification tree algorithm to generate classifiers from the bank credit card data and the logistic regression model to estimate the relative contribution of predictor variables to the classification rule. It can effectively identify the most predictive subsets and estimate the probability of any credit applicant being a good risk given the characteristics vector. Rwei-Ju CHUANG 莊瑞珠 2008 學位論文 ; thesis 127 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 輔仁大學 === 應用統計學研究所 === 96 === The objective of this study is to build a credit scoring system to guard against management risk for the credit-issue financial corporations. A pragmatic devise merging the decision tree CHAID techniques with logistic regression is proposed and applied to the model building. This twofold approach uses the classification tree algorithm to generate classifiers from the bank credit card data and the logistic regression model to estimate the relative contribution of predictor variables to the classification rule. It can effectively identify the most predictive subsets and estimate the probability of any credit applicant being a good risk given the characteristics vector.
author2	Rwei-Ju CHUANG
author_facet	Rwei-Ju CHUANG Hsu Jung-Chieh 許榮傑
author	Hsu Jung-Chieh 許榮傑
spellingShingle	Hsu Jung-Chieh 許榮傑 The Building of Credit Card Scoring Via Logistic Regression Model Combined with Decision Tree
author_sort	Hsu Jung-Chieh
title	The Building of Credit Card Scoring Via Logistic Regression Model Combined with Decision Tree
title_short	The Building of Credit Card Scoring Via Logistic Regression Model Combined with Decision Tree
title_full	The Building of Credit Card Scoring Via Logistic Regression Model Combined with Decision Tree
title_fullStr	The Building of Credit Card Scoring Via Logistic Regression Model Combined with Decision Tree
title_full_unstemmed	The Building of Credit Card Scoring Via Logistic Regression Model Combined with Decision Tree
title_sort	building of credit card scoring via logistic regression model combined with decision tree
publishDate	2008
url	http://ndltd.ncl.edu.tw/handle/93622354485551178617
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The Building of Credit Card Scoring Via Logistic Regression Model Combined with Decision Tree

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