Professor of Data Science at Münster School of Business
Steps for Score Card construction using Logistic Regression (Szepannek 2017)
Steps for Score Card construction using Logistic Regression (Szepannek 2017)
Manual binning allows for
Manual binning allows for
... and means a lot of manual work
We tested a couple of Machine Learning algorithms ...
randomForest
)gbm
)xgboost
)svm
)rms
)... and also two AutoML frameworks to beat the Score Card
h2o
)mljar
)There are many model-agnostic methods for interpretable ML today; see Molnar (2019) for a good overview.
rms
by Harrell Jr (2019)
For comparison of explainability, we choose
Biecek, P. (2018). "DALEX: explainers for complex predictive models". In: Journal of Machine Learning Research 19.84, pp. 1-5.
Biecek, P, M. Tatarynowicz, K. Romaszko, and M. Urbański (2019). modelDown: Make Static HTML Website for Predictive Models. R package version 1.0.1. URL: https://CRAN.R-project.org/package=modelDown.
Bischl, B., T. Kühn, and G. Szepannek (2014). "On Class Imbalance Correction for Classification Algorithms in Credit Scoring". In: Operations Research Proceedings. Ed. by M. Löbbecke, A. Koster, L. P., M. R., P. B. and G. Walther. , pp. 37-43.
FICO (2019). xML Challenge. Online. URL: https://community.fico.com/s/explainable-machine-learning-challenge.
Harrell Jr, F. E. (2019). rms: Regression Modeling Strategies. R package version 5.1-3.1. URL: https://CRAN.R-project.org/package=rms.
Lessmann, S, B. Baesens, H. Seow, and L. Thomas (2015). "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research". In: European Journal of Operational Research 247.1, pp. 124-136.
Molnar, C. (2019). Interpretable Machine Learning. A Guide for Making Black Box Models Explainable. URL: https://christophm.github.io/interpretable-ml-book/.
Molnar, C, B. Bischl, and G. Casalicchio (2018). "iml: An R package for Interpretable Machine Learning". In: Journal Of Statistical Software 3.26, p. 786. URL: http://joss.theoj.org/papers/10.21105/joss.00786.
Szepannek, G. (2017b). A Framework for Scorecard Modelling using R. CSCC 2017.
Szepannek, G. (2017a). "On the Practical Relevance of Modern Machine Learning Algorithms for Credit Scoring Applications". In: WIAS Report Series 29, pp. 88-96.
Professor of Data Science
Münster School of Business
FH Münster - University of Applied Sciences -
Corrensstraße 25, Room C521
D-48149 Münster
Tel: +49 251 83 65615
E-Mail: michael.buecker@fh-muenster.de
https://buecker.ms
Professor of Data Science
Münster School of Business
FH Münster - University of Applied Sciences -
Corrensstraße 25, Room C521
D-48149 Münster
Tel: +49 251 83 65615
E-Mail: michael.buecker@fh-muenster.de
https://buecker.ms
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