Search Results for author: Gero Szepannek

Found 3 papers, 2 papers with code

Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring

1 code implementation28 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.

BIG-bench Machine Learning

An Overview on the Landscape of R Packages for Credit Scoring

1 code implementation21 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.

How Much Can We See? A Note on Quantifying Explainability of Machine Learning Models

no code implementations29 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.

BIG-bench Machine Learning

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