Giuseppe Casalicchio


I finished my PhD at the Working Group Computational Statistics in early 2019 focussing on bechmark experiments, evaluation of machine learning models, and the emerging field of interpretable machine learning. During my PhD, I was also heavily involved in the contribution and development of a variety software projects.

Since March 2019, I am supporting this chair within the framework of the Munich Center for Machine Learning (MCML) as education manager of the Data Science Certificate Program and as PostDoc leading the interpretable machine learning research group. Besides of this, I am part-time CEO of a LMU spin-off education company called Essential Data Science Training GmbH.


Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 344, 3rd floor

Phone: +49 89 2180 3196

giuseppe.casalicchio [at]

Research Interests

You can find me on



  1. Molnar C, Casalicchio G, Bischl B (2020) Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. In: In: Cellier P , In: Driessens K (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, pp. 193–204. Springer International Publishing, Cham.
    DOI: 10.1007/978-3-030-43823-4_17 | PDF: arXiv:1904.03867
  2. Scholbeck CA, Molnar C, Heumann C, Bischl B, Casalicchio G (2020) Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. In: In: Cellier P , In: Driessens K (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, pp. 205–216. Springer International Publishing, Cham.
    DOI: 10.1007/978-3-030-43823-4_18 | PDF: arXiv:1904.03959
  3. Molnar C, König G, Bischl B, Casalicchio G (2020) Model-agnostic Feature Importance and Effects with Dependent Features–A Conditional Subgroup Approach. arXiv preprint arXiv:2006.04628.
    LINK: arXiv:2006.04628
  4. Molnar C, König G, Herbinger J et al. (2020) Pitfalls to Avoid when Interpreting Machine Learning Models. arXiv preprint arXiv:2007.04131.
    LINK: arXiv:2007.04131
  5. Lang M, Binder M, Richter J et al. (2019) mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software 4, 1903.
    DOI: 10.21105/joss.01903 | PDF: JOSS
  6. Au Q, Schalk D, Casalicchio G, Schoedel R, Stachl C, Bischl B (2019) Component-Wise Boosting of Targets for Multi-Output Prediction. arXiv preprint arXiv:1904.03943.
    LINK: arXiv:1904.03943
  7. Casalicchio G, Molnar C, Bischl B (2019) Visualizing the Feature Importance for Black Box Models. In: In: Berlingerio M , In: Bonchi F , In: Gärtner T , In: Hurley N , In: Ifrim G (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2018, pp. 655–670. Springer International Publishing, Cham.
    DOI: 10.1007/978-3-030-10925-7_40 | PDF: arXiv:1804.06620
  8. Molnar C, Casalicchio G, Bischl B (2018) iml: An R package for Interpretable Machine Learning. The Journal of Open Source Software 3, 786.
    DOI: 10.21105/joss.00786 | PDF: JOSS
  9. Bischl B, Casalicchio G, Feurer M et al. (2019) OpenML benchmarking suites and the OpenML100. arXiv preprint arXiv:1708.03731.
    LINK: arXiv:1708.03731
  10. Casalicchio G, Lesaffre E, Küchenhoff H, Bruyneel L (2017) Nonlinear Analysis to Detect if Excellent Nursing Work Environments Have Highest Well-Being. Journal of Nursing Scholarship 49, 537–547.
    DOI: 10.1111/jnu.12317 | PDF: Researchgate
  11. Casalicchio G, Bossek J, Lang M et al. (2017) OpenML: An R package to connect to the machine learning platform OpenML. Computational Statistics, 1–15.
    DOI: 10.1007/s00180-017-0742-2 | PDF: arXiv:1701.01293
  12. Probst P, Au Q, Casalicchio G, Stachl C, Bischl B (2017) Multilabel Classification with R Package mlr. The R Journal 9, 352–369.
    DOI: 10.32614/RJ-2017-012 | PDF: PDF
  13. Bischl B, Lang M, Kotthoff L et al. (2016) mlr: Machine Learning in R. The Journal of Machine Learning Research 17, 5938–5942.
    LINK: JMLR 17(1)
  14. Casalicchio G, Bischl B, Boulesteix A-L, Schmid M (2015) The residual-based predictiveness curve: A visual tool to assess the performance of prediction models. Biometrics 72, 392–401.
    DOI: 10.1111/biom.12455 | PDF: Researchgate
  15. Casalicchio G, Tutz G, Schauberger G (2015) Subject-specific Bradley–Terry–Luce models with implicit variable selection. Statistical Modelling 15, 526–547.
    DOI: 10.1177/1471082X15571817 | PDF: Researchgate
  16. Bergmann S, Ziegler N, Bartels T et al. (2013) Prevalence and severity of foot pad alterations in German turkey poults during the early rearing phase. Poultry science 92, 1171–1176.
    DOI: 10.3382/ps.2012-02851 | PDF: Researchgate
  17. Ziegler N, Bergmann S, Huebei J et al. (2013) Climate parameters and the influence on the foot pad health status of fattening turkeys BUT 6 during the early rearing phase. BERLINER UND MUNCHENER TIERARZTLICHE WOCHENSCHRIFT 126, 181–188.
    LINK: Researchgate