15 citations · 23 across the 3 of their papers we have counts for
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stat.ML2020★ 15 cited
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring
Michael Bücker, Gero Szepannek, Alicja Gosiewska +1
A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus…
stat.ML2019★ 6 cited
How Much Can We See? A Note on Quantifying Explainability of Machine Learning Models
Gero Szepannek
One of the most popular approaches to understanding feature effects of modern black box machine learning models are partial dependence plots (PDP). These plots are easy to understa…