| 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 | 1418* | 2015 |
| mlr: Machine Learning in R B Bischl, M Lang, L Kotthoff, J Schiffner, J Richter, E Studerus, ... Journal of Machine Learning Research 17 (170), 1-5, 2016 | 910 | 2016 |
| Interpretable machine learning–a brief history, state-of-the-art and challenges C Molnar, G Casalicchio, B Bischl Joint European conference on machine learning and knowledge discovery in …, 2020 | 588 | 2020 |
| iml: An R package for interpretable machine learning C Molnar, G Casalicchio, B Bischl Journal of Open Source Software 3 (26), 786, 2018 | 384 | 2018 |
| 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 | 292* | 2019 |
| Visualizing the feature importance for black box models G Casalicchio, C Molnar, B Bischl Machine Learning and Knowledge Discovery in Databases: European Conference …, 2019 | 255 | 2019 |
| General pitfalls of model-agnostic interpretation methods for machine learning models C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ... International Workshop on Extending Explainable AI Beyond Deep Models and …, 2020 | 165 | 2020 |
| OpenML Benchmarking Suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... NeurIPS 2021 Datasets and Benchmarks Track, 2021 | 132* | 2021 |
| Quantifying model complexity via functional decomposition for better post-hoc interpretability C Molnar, G Casalicchio, B Bischl Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, 193--204, 2019 | 101* | 2019 |
| Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach C Molnar, G König, B Bischl, G Casalicchio Data Mining and Knowledge Discovery, 1-39, 2023 | 83 | 2023 |
| OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... stat 1050, 11, 2017 | 76 | 2017 |
| 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, 977-991, 2019 | 68 | 2019 |
| Relating the partial dependence plot and permutation feature importance to the data generating process C Molnar, T Freiesleben, G König, J Herbinger, T Reisinger, ... World Conference on Explainable Artificial Intelligence, 456-479, 2023 | 61 | 2023 |
| Explaining hyperparameter optimization via partial dependence plots J Moosbauer, J Herbinger, G Casalicchio, M Lindauer, B Bischl Advances in Neural Information Processing Systems 34, 2280-2291, 2021 | 61* | 2021 |
| Pitfalls to avoid when interpreting machine learning models C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ... Accepted at the ICML 2020 workshop XXAI: Extending Explainable AI Beyond …, 2020 | 55 | 2020 |
| Sampling, intervention, prediction, aggregation: A generalized framework for model-agnostic interpretations CA Scholbeck, C Molnar, C Heumann, B Bischl, G Casalicchio Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, 205--216, 2019 | 43 | 2019 |
| Grouped feature importance and combined features effect plot Q Au, J Herbinger, C Stachl, B Bischl, G Casalicchio Data Mining and Knowledge Discovery 36 (4), 1401-1450, 2022 | 39 | 2022 |
| 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 | 38 | 2013 |
| Multilabel classification with R package mlr P Probst, Q Au, G Casalicchio, C Stachl, B Bischl The R Journal 9 (1), 352-369, 2017 | 31 | 2017 |
| Over‐optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results C Nießl, M Herrmann, C Wiedemann, G Casalicchio, AL Boulesteix Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12 (2 …, 2022 | 26 | 2022 |
Bernd BischlChair of Statistical Learning and Data Science, LMU Munich在 stat.uni-muenchen.de 的电子邮件经过验证
