Open Access. Powered by Scholars. Published by Universities.®
![Digital Commons Network](http://assets.bepress.com/20200205/img/dcn/DCsunburst.png)
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Action (1)
- Adventure (1)
- Bankruptcy Prediction (1)
- Discrimination Ability (1)
- Event Rate (1)
-
- Firmographics (1)
- Humanistic Psychology (1)
- Mindfulness (1)
- Philosophy (1)
- Prace ze studentami (in Polish) (1)
- Public Records (1)
- Science Fiction (1)
- Self Development (1)
- Terms—Past Due; Credit Risk Model; Logistic Regression; Decision Trees; Neural Networks; (1)
- Transpersonal Psychology (1)
- Well Being (1)
Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
"Being Mindful" And Becoming A "Harmony Worker" During Unsettling Times.Docx
"Being Mindful" And Becoming A "Harmony Worker" During Unsettling Times.Docx
Carroy U "Cuf" Ferguson, Ph.D.
Shining A Humanistic Light On Racism.Docx
Shining A Humanistic Light On Racism.Docx
Carroy U "Cuf" Ferguson, Ph.D.
123movies!@! Watch [ Captain Marvel ] Hd-2019-Full Movie Online Free Hd, Jokodok Koplok
123movies!@! Watch [ Captain Marvel ] Hd-2019-Full Movie Online Free Hd, Jokodok Koplok
jokodok koplok
No abstract provided.
Influence Of The Event Rate On Discrimination Abilities Of Bankruptcy Prediction Models, Lili Zhang, Jennifer Priestley, Xuelei Ni
Influence Of The Event Rate On Discrimination Abilities Of Bankruptcy Prediction Models, Lili Zhang, Jennifer Priestley, Xuelei Ni
Jennifer L. Priestley
In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy prediction models. First the statistical association and significance of public records and firmographics indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. Under different event rates, models were comprehensively evaluated and compared …
A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.
A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.
Jennifer L. Priestley
Credit risk modeling has carried a variety of research interest in previous literature, and recent studies have shown that machine learning methods achieved better performance than conventional statistical ones. This study applies decision tree which is a robust advanced credit risk model to predict the commercial non-financial past-due problem with better critical power and accuracy. In addition, we examine the performance with logistic regression analysis, decision trees, and neural networks. The experimenting results confirm that decision trees improve upon other methods. Also, we find some interesting factors that impact the commercials’ non-financial past-due payment.
2018 Florida Data Science For Social Good - Annual Report, Karthikeyan Umapathy, F. Dan Richard
2018 Florida Data Science For Social Good - Annual Report, Karthikeyan Umapathy, F. Dan Richard
Karthikeyan Umapathy
Call For Abstracts - Resrb 2019, July 8-9, Wrocław, Poland, Wojciech M. Budzianowski
Call For Abstracts - Resrb 2019, July 8-9, Wrocław, Poland, Wojciech M. Budzianowski
Wojciech Budzianowski
No abstract provided.