INTERPRETABLE MACHINE LEARNING CREDIT SCORING MODEL / (Record no. 382999)
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000 -LEADER | |
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fixed length control field | 02149nam a2200229 4500 |
003 - CONTROL NUMBER IDENTIFIER | |
control field | APU |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20230625192118.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 200218b2019 ||||| |||| 00| 0 eng d |
050 ## - LIBRARY OF CONGRESS CALL NUMBER | |
Classification number | PM-31-51 |
100 0# - MAIN ENTRY--PERSONAL NAME | |
Personal name | FOO YEONG JIN (TP044538) |
9 (RLIN) | 45342 |
245 10 - TITLE STATEMENT | |
Title | INTERPRETABLE MACHINE LEARNING CREDIT SCORING MODEL / |
Statement of responsibility, etc | FOO YEONG JIN. |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
Place of publication, distribution, etc | Kuala Lumpur : |
Name of publisher, distributor, etc | Asia Pacific University, |
Date of publication, distribution, etc | 2019. |
300 ## - PHYSICAL DESCRIPTION | |
Extent | xiii, 55 pages : |
Other physical details | illustrations ; |
Dimensions | 30 cm. |
502 ## - DISSERTATION NOTE | |
Dissertation note | A capstone project submitted in fulfillment of the requirement for the award of the degree of Master of Science in Data Science and Business Analytics (UCMP1701DSBA) |
520 ## - SUMMARY, ETC. | |
Summary, etc | This study divided into two distinct part to accurately assess the interpretability of the proposed model. The first part is to develop a credit scoring model using the black-box algorithm, and the second part is to develop an interpretable model to interpret the black-box model. The extreme gradient boosting (xgboost) is used as the black-box algorithm and Local Interpretable Model-agnostic Explanations (LIME) is used as interpretation model. For the evaluation, this study is using two different assessment to measure the interpretability of the interpretation model. The first assessment is to measure the consistency through the weight assigned to each independent variable in both training and validation data. The second assessment is the interpretability, which signage assigned to each independent variable is used to compare against the finding from the literature. The result shows that the proposed model is achieved the desire consistency and interpretability. The difference between training and validation data is less than 5%. The interpretability is also same as the finding gathered from the literature. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Credit scoring systems. |
9 (RLIN) | 45343 |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Machine learning. |
9 (RLIN) | 45344 |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Regression analysis |
General subdivision | Data processing. |
9 (RLIN) | 45345 |
700 0# - ADDED ENTRY--PERSONAL NAME | |
Personal name | Dr. V. Sivakumar |
Relator term | Supervisor. |
-- | 48427 |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://cas.apiit.edu.my/cas/login?service=https://library.apu.edu.my/apres/ |
Link text | Available in APres |
Public note | - Requires login to view full text. |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Source of classification or shelving scheme | |
Koha item type | Masters Theses |
Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Use restrictions | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Full call number | Barcode | Date last seen | Copy number | Koha item type | Public note |
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Not Withdrawn | Available | Not Damaged | Restricted access | Not for loan | Masters Theses | APU Library | APU Library | Reference Collection | 18/02/2020 | PM-31-51 | 00017666 | 18/02/2020 | 1 | Reference | Available in APres |