1 code implementation • 28 Sep 2020 • Michael Bücker, Gero Szepannek, Alicja Gosiewska, Przemyslaw Biecek
This paper works out different dimensions that have to be considered for making credit scoring models understandable and presents a framework for making ``black box'' machine learning models transparent, auditable and explainable.
1 code implementation • 21 Jun 2020 • Gero Szepannek
The credit scoring industry has a long tradition of using statistical tools for loan default probability prediction and domain specific standards have been established long before the hype of machine learning.
no code implementations • 29 Oct 2019 • Gero Szepannek
': A framework is developed to quantify the explainability of arbitrary machine learning models, i. e. up to what degree the visualization as given by a PDP is able to explain the predictions of the model.