Asia Pacific University Library catalogue


INTERPRETABLE MACHINE LEARNING CREDIT SCORING MODEL / (Record no. 382999)

000 -LEADER
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
Holdings
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
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