Giuseppe Casalicchio
Giuseppe Casalicchio
Postdoctoral Researcher, LMU Munich, Munich Center for Machine Learning
Verified email at - Homepage
Cited by
Cited by
OpenML: A networked science platform for machine learning
J Vanschoren, JN van Rijn, B Bischl, G Casalicchio, M Lang, M Feurer
2015 ICML Workshop on Machine Learning Open Source Software (MLOSS 2015)., 2015
mlr: Machine Learning in R
B Bischl, M Lang, L Kotthoff, J Schiffner, J Richter, E Studerus, ...
The Journal of Machine Learning Research 17 (1), 5938-5942, 2016
iml: An R package for interpretable machine learning
C Molnar, G Casalicchio, B Bischl
Journal of Open Source Software 3 (26), 786, 2018
OpenML benchmarking suites and the OpenML100
B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ...
stat 1050, 11, 2017
Visualizing the feature importance for black box models
G Casalicchio, C Molnar, B Bischl
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018
OpenML: An R package to connect to the machine learning platform OpenML
G Casalicchio, J Bossek, M Lang, D Kirchhoff, P Kerschke, B Hofner, ...
Computational Statistics 34 (3), 977-991, 2019
Prevalence and severity of foot pad alterations in German turkey poults during the early rearing phase
S Bergmann, N Ziegler, T Bartels, J Hübel, C Schumacher, E Rauch, ...
Poultry science 92 (5), 1171-1176, 2013
Quantifying model complexity via functional decomposition for better post-hoc interpretability
C Molnar, G Casalicchio, B Bischl
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019
Multilabel classification with R package mlr
P Probst, Q Au, G Casalicchio, C Stachl, B Bischl
arXiv preprint arXiv:1703.08991, 2017
mlr3: A modern object-oriented machine learning framework in R
M Lang, M Binder, J Richter, P Schratz, F Pfisterer, S Coors, Q Au, ...
Journal of Open Source Software 4 (44), 1903, 2019
Nonlinear analysis to detect if excellent nursing work environments have highest well‐being
G Casalicchio, E Lesaffre, H Küchenhoff, L Bruyneel
Journal of Nursing Scholarship 49 (5), 537-547, 2017
Sampling, intervention, prediction, aggregation: A generalized framework for model-agnostic interpretations
CA Scholbeck, C Molnar, C Heumann, B Bischl, G Casalicchio
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019
Subject-specific Bradley–Terry–Luce models with implicit variable selection
G Casalicchio, G Tutz, G Schauberger
Statistical Modelling 15 (6), 526-547, 2015
Model-agnostic Feature Importance and Effects with Dependent Features--A Conditional Subgroup Approach
C Molnar, G König, B Bischl, G Casalicchio
arXiv preprint arXiv:2006.04628, 2020
ordBTL: Modelling comparison data with ordinal response
G Casalicchio
R package version 0.8, 2013
The residual‐based predictiveness curve: A visual tool to assess the performance of prediction models
G Casalicchio, B Bischl, AL Boulesteix, M Schmid
Biometrics 72 (2), 392-401, 2016
Climate parameters and the influence on the footpad health status of fattening turkeys BUT 6 during the early rearing phase.
N Ziegler, S Bergmann, J Hübel, T Bartels, C Schumacher, A Bender, ...
Berliner und Münchener Tierärztliche Wochenschrift 126 (5/6), 181-188, 2013
Interpretable Machine Learning--A Brief History, State-of-the-Art and Challenges
C Molnar, G Casalicchio, B Bischl
arXiv preprint arXiv:2010.09337, 2020
Pitfalls to avoid when interpreting machine learning models
C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ...
arXiv preprint arXiv:2007.04131, 2020
On benchmark experiments and visualization methods for the evaluation and interpretation of machine learning models
G Casalicchio
lmu, 2019
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