000			02149nam a2200229 4500
003			APU
005			20230625192118.0
008			200218b2019 ||||| |||| 00| 0 eng d
050			_aPM-31-51
100	0		_aFOO YEONG JIN (TP044538) _945342
245	1	0	_aINTERPRETABLE MACHINE LEARNING CREDIT SCORING MODEL / _cFOO YEONG JIN.
260			_aKuala Lumpur : _bAsia Pacific University, _c2019.
300			_axiii, 55 pages : _billustrations ; _c30 cm.
502			_aA 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			_aThis 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	_aCredit scoring systems. _945343
650		0	_aMachine learning. _945344
650		0	_aRegression analysis _xData processing. _945345
700	0		_aDr. V. Sivakumar _eSupervisor. _948427
856	4	0	_uhttps://cas.apiit.edu.my/cas/login?service=https://library.apu.edu.my/apres/ _yAvailable in APres _z- Requires login to view full text.
942			_2lcc _cMasters Theses
999			_c382999 _d382999