An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market
详细信息 查看全文
- 作者：Trevor Fitzpatrick ; 1 ; a ; b ; T.Fitzpatrick@soton.ac.uk" class="auth_mail" title="E-mail the corresponding author ; Christophe Muesa ; C.Mues@soton.ac.uk" class="auth_mail" title="E-mail the corresponding author
- 关键词：Boosting ; Random forests ; Semi-parametric models ; Mortgages ; Credit scoring
- 刊名：European Journal of Operational Research
- 出版年：2016
- 出版时间：1 March 2016
- 年：2016
- 卷：249
- 期：2
- 页码：427-439
- 全文大小：871 K
文摘
- •
We evaluate default prediction performance of machine learning/regression models.
- •
Including boosted trees, random forests, penalised linear/semi-parametric logistic regression.
- •
Using data on over 300,000 residential mortgage loans.
- •
The results indicate varying degrees of predictive power.
- •
Statistical tests suggest boosted regression trees outperform penalised logistic regression.