Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects
Mercredi | 2018-02-08 Salle B103 – 12h00 Sullivan HUE – Elena-Ivona DUMITRESCU – Christophe HURLIN – Sessi TOKPAVI Decision trees and related ensemble methods like random forest are state-of-the-art tools in the eld of machine learning for predictive regression and classi cation. However, they lack interpretability and can be less relevant in credit scoring applications, where decision-makers and regulators need a transparent linear score function that usually corresponds to the link function in logistic regressions. In this paper, we propose […]