03755ntm a22005657i 4500 000700256 CZ-PrVSE 20230618075638.0 m d cr n|||||||||| 230618s2023 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Nguyen, Petr ISIS:130817 dis Aplikace modelů strojového učení v rámci modelování kreditního rizika eng Application of Machine Learning Models within Credit Risk Modelling / Petr Nguyen 2023 ?? stran : digital, PDF soubor Vedoucí práce: Petr Teplý Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví, 2023 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2023 This Master’s thesis deals with the custom machine learning implementation framework that was developed in Python and applied to the application scoring data of US home equity loans (HMEQ). The ML framework involves eight classification models, namely Logistic Regression, Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbors, Random Forest, Gradient Boosting, Support Vector Machine, and Neural Network. It further consists of data exploration, data preprocessing using ADASYN oversampling and Optimal Binning with Weight-of-Evidence, a custom feature selection algorithm that utilizes both Bayesian Optimization and Forward Sequential Feature Selection, and a custom model selection algorithm employed based on Bayesian Optimization and weighted ranking of individual metric ranks. In this thesis, metrics such as F1 score, MCC, AUC, Kolmogorov-Smirnov Distance, Somers’ D, and others, are evaluated. Instead of using the standard classification threshold of 0.5, an optimal threshold is calculated using Youden index. The final model is Gradient Boosting trained on the features selected by Neural Network. Such model is further recalibrated and evaluated using both model performance assessment and black-box model explainability inspection. The final model is deployed as a web application using Flask and HTML, which requires filling in the loan application form and outputs the loan approval result, probability of default, and LIME plot, i.e., local explainability of the black-box model around the single prediction. Způsob přístupu: Internet bankovnictví a pojišťovnictví [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas Machine Learning Probability of Default Credit Risk Loans Python Web Application Bayesian Optimization Teplý, Petr, 1977- mzk2008486526 ths Palán, Luděk ISIS:56874 opn Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví kn20010709398 dgg https://insis.vse.cz/zp/80349/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/80349 Hlavní práce https://insis.vse.cz/zp/80349/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/80349/posudek/oponent/78704 Oponentura https://insis.vse.cz/zp/80349/priloha/26408 Přiloha k práci https://insis.vse.cz/zp/80349/priloha/26409 Přiloha k práci https://insis.vse.cz/zp/80349/podrobnosti dc:identifier NEPOSILAT VSKP vse80349 230616 80349