Logistic regression improvements for credit scoring development - Pravdin, Nikolai; Witzany, Jiří, 1966-; Panoš, Jiří; Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví (2022)
The current Study investigates the impact of non-linear relationships between input variables during credit risk modeling. Particularly, it is meant to analyze the burden of variables’ non-linear behavior on the current modeling benchmark – Logistic regression – using some general set of retail clients’ data. The main focus done on application of alternative solutions to efficiently mitigate nonlinearity’s influence. Among them is well-known strong learner - Random Forest, as well as relatively new approach – Penalized Logit Tree Regression – a transparent and intuitive combination of Logit and Decision Trees. Mentioned methods are modeled on both the real-world data and specific simulated data with step-by-step description of the process. Finally, results are analytically compared using popular performance metrics like Gini Index and K-S statistic.
Vedoucí práce: Jiří Witzany
Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví, 2022
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2022
Způsob přístupu: Internet
Keywords: finanční inženýrství [obor dipl. práce]; diplomové práce; master's theses