Research Group Anne-Laure Boulesteix
Anne-Laure Boulesteix
Prof. Dr.
Principal Investigator
Anne-Laure Boulesteix
is Professor for Biometry in Molecular Medicine at LMU Munich.
Her working group focuses on developing advanced biostatistical methods for prediction modeling and high-dimensional data analysis, with applications in biomedical research, especially omics data. Additionally, they engage in metascience, examining research practices to improve study reliability and address issues like selective reporting and researchers’ degrees of freedom.
Team members @MCML
PostDocs
Moritz Herrmann
Dr.
Transfer Coordinator
→ Group Anne-Laure Boulesteix
Biometry in Molecular Medicine
Roman Hornung
Dr.
→ Group Anne-Laure Boulesteix
Biometry in Molecular Medicine
PhD Students
Mathilde Dicaire-Cartier
→ Group Anne-Laure Boulesteix
Biometry in Molecular Medicine
Nicole Ellenbach
→ Group Anne-Laure Boulesteix
Biometry in Molecular Medicine
Julian Lange
→ Group Anne-Laure Boulesteix
Biometry in Molecular Medicine
Maximilian Mandl
→ Group Anne-Laure Boulesteix
Biometry in Molecular Medicine
Christina Sauer (née Nießl)
→ Group Anne-Laure Boulesteix
Biometry in Molecular Medicine
Hannah Schulz-Kümpel
→ Group Anne-Laure Boulesteix
Biometry in Molecular Medicine
Milena Wünsch
→ Group Anne-Laure Boulesteix
Biometry in Molecular Medicine
Recent News @MCML
Publications @MCML
2025
[59]
M. Abrahamowicz • M.-E. Beauchamp • A.-L. Boulesteix • T. P. Morris • W. Sauerbrei • J. S. Kaufman • o. b. o. t. STRATOS Simulation Panel
Efficient Computation of Image Persistence.
Discrete and Computational Geometry. Oct. 2025. DOI
Efficient Computation of Image Persistence.
Discrete and Computational Geometry. Oct. 2025. DOI
[58]
D. Dobler • H. Binder • A.-L. Boulesteix • J.-B. Igelmann • D. Köhler • U. Mansmann • M. Pauly • A. Scherag • M. Schmid • A. A. Tawil • S. Weber
ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations.
Statistics in Medicine 44.23-24. Oct. 2025. DOI
ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations.
Statistics in Medicine 44.23-24. Oct. 2025. DOI
[57]
M. Wünsch • M. Noltenius • M. Mohr • T. P. Morris • A.-L. Boulesteix
Rethinking the Handling of Method Failure in Comparison Studies.
Statistics in Medicine. Oct. 2025. DOI
Rethinking the Handling of Method Failure in Comparison Studies.
Statistics in Medicine. Oct. 2025. DOI
[56]
A.-L. Boulesteix • P. Callahan • L. Hanssum • V. Gaertner • E. Hoster
Bridging the Gap Between Methodological Research and Statistical Practice: Toward Translational Simulation Research.
Preprint (Oct. 2025). arXiv
Bridging the Gap Between Methodological Research and Statistical Practice: Toward Translational Simulation Research.
Preprint (Oct. 2025). arXiv
[55]
A. S. Gutmann • M. M. Mandl • C. Rieder • D. J. Hoechter • K. Dietz • B. P. Geisler • A.-L. Boulesteix • R. Tomasi • L. C. Hinske
Comparing supervised machine learning algorithms for the prediction of partial arterial pressure of oxygen during craniotomy.
BMC Medical Informatics and Decision Making 25.326. Sep. 2025. DOI
Comparing supervised machine learning algorithms for the prediction of partial arterial pressure of oxygen during craniotomy.
BMC Medical Informatics and Decision Making 25.326. Sep. 2025. DOI
[54]
M. M. Mandl
Addressing researcher degrees of freedom in applications, methodological research, and teaching.
Dissertation LMU München. Jul. 2025. DOI
Addressing researcher degrees of freedom in applications, methodological research, and teaching.
Dissertation LMU München. Jul. 2025. DOI
[53]
M. M. Mandl • A.-L. Boulesteix • S. Burgess • V. Zuber
Outlier Detection in Mendelian Randomization.
Statistics in Medicine 44.15-17. Jul. 2025. DOI
Outlier Detection in Mendelian Randomization.
Statistics in Medicine 44.15-17. Jul. 2025. DOI
[52]
C. Sauer • F. J. D. Lange • M. Thurow • I. Dormuth • A.-L. Boulesteix
Statistical parametric simulation studies based on real data.
Preprint (Apr. 2025). arXiv
Statistical parametric simulation studies based on real data.
Preprint (Apr. 2025). arXiv
[51]
R. Hornung • M. Nalenz • L. Schneider • A. Bender • L. Bothmann • F. Dumpert • B. Bischl • T. Augustin • A.-L. Boulesteix
Evaluating Machine Learning Models in Non-Standard Settings: An Overview and New Findings.
Statistical Science. Mar. 2025. To be published. Preprint available. arXiv URL
Evaluating Machine Learning Models in Non-Standard Settings: An Overview and New Findings.
Statistical Science. Mar. 2025. To be published. Preprint available. arXiv URL
[50]
F. J. D. Lange • J. C. Wilcke • S. Hoffmann • M. Herrmann • A.-L. Boulesteix
On 'confirmatory' methodological research in statistics and related fields.
Preprint (Mar. 2025). arXiv
On 'confirmatory' methodological research in statistics and related fields.
Preprint (Mar. 2025). arXiv
[49]
M. M. Mandl • F. Weber • T. Wöhrle • A.-L. Boulesteix
The impact of the storytelling fallacy on real data examples in methodological research.
Preprint (Mar. 2025). arXiv
The impact of the storytelling fallacy on real data examples in methodological research.
Preprint (Mar. 2025). arXiv
[48]
R. Rehms • N. Ellenbach • V. Deffner • S. Hoffmann
Addressing complex structures of measurement error arising in the exposure assessment in occupational epidemiology using a Bayesian hierarchical approach.
Preprint (Mar. 2025). arXiv
Addressing complex structures of measurement error arising in the exposure assessment in occupational epidemiology using a Bayesian hierarchical approach.
Preprint (Mar. 2025). arXiv
[47]
M. Wünsch • C. Sauer • M. Herrmann • L. C. Hinske • A.-L. Boulesteix
To tweak or not to tweak. How exploiting flexibilities in gene set analysis leads to over-optimism.
Biometrical Journal 67.1. Feb. 2025. DOI
To tweak or not to tweak. How exploiting flexibilities in gene set analysis leads to over-optimism.
Biometrical Journal 67.1. Feb. 2025. DOI
[46]
M. Abrahamowicz • M.-E. Beauchamp • A.-L. Boulesteix • T. P. Morris • W. Sauerbrei • J. S. Kaufman • o. b. o. t. STRATOS Simulation Panel
Data-driven simulations to assess the impact of study imperfections in time-to-event analyses.
American Journal of Epidemiology 194.1. Jan. 2025. DOI
Data-driven simulations to assess the impact of study imperfections in time-to-event analyses.
American Journal of Epidemiology 194.1. Jan. 2025. DOI
[45]
H. Schulz-Kümpel • S. Fischer • T. Nagler • A.-L. Boulesteix • B. Bischl • R. Hornung
Constructing Confidence Intervals for 'the' Generalization Error – a Comprehensive Benchmark Study.
Journal of Data-centric Machine Learning Research 2.6. Jan. 2025. To be published. Preprint available. URL
Constructing Confidence Intervals for 'the' Generalization Error – a Comprehensive Benchmark Study.
Journal of Data-centric Machine Learning Research 2.6. Jan. 2025. To be published. Preprint available. URL
2024
[44]
C. Sauer • A.-L. Boulesteix • L. Hanßum • F. Hodiamont • C. Bausewein • T. Ullmann
Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications.
Preprint (Dec. 2024). arXiv
Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications.
Preprint (Dec. 2024). arXiv
[43]
T. Woehrle • F. Pfeiffer • M. M. Mandl • W. Sobtzick • J. Heitzer • A. Krstova • L. Kamm • M. Feuerecker • D. Moser • M. Klein • B. Aulinger • M. Dolch • A.-L. Boulesteix • D. Lanz • A. Choukér
Point-of-care breath sample analysis by semiconductor-based E-Nose technology discriminates non-infected subjects from SARS-CoV-2 pneumonia patients: a multi-analyst experiment.
MedComm 5.11. Nov. 2024. DOI
Point-of-care breath sample analysis by semiconductor-based E-Nose technology discriminates non-infected subjects from SARS-CoV-2 pneumonia patients: a multi-analyst experiment.
MedComm 5.11. Nov. 2024. DOI
[42]
L. Barreñada • P. Dhiman • D. Timmerman • A.-L. Boulesteix • B. Van Calster
Understanding overfitting in random forest for probability estimation: a visualization and simulation study.
Diagnostic and Prognostic Research 8.14. Sep. 2024. DOI
Understanding overfitting in random forest for probability estimation: a visualization and simulation study.
Diagnostic and Prognostic Research 8.14. Sep. 2024. DOI
[41]
Y. Li • T. Herold • U. Mansmann • R. Hornung
Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study.
Earth System Science Data 24.244. Sep. 2024. DOI
Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study.
Earth System Science Data 24.244. Sep. 2024. DOI
[40]
R. Hornung • A. Hapfelmeier
Multi forests: Variable importance for multi-class outcomes.
Preprint (Sep. 2024). arXiv
Multi forests: Variable importance for multi-class outcomes.
Preprint (Sep. 2024). arXiv
[39]
M. Herrmann
Dimensionality and Distance: Curse or Blessing? Geometrical Aspects of Nearest Neighbor Computation in High-Dimensional Data.
Statistical Computing 2024 - 54. Arbeitstagung der Arbeitsgruppen Statistical Computing, Klassifikation und Datenanalyse in den Biowissenschaften. Günzburg, Germany, Jul 28-31, 2024. PDF
Dimensionality and Distance: Curse or Blessing? Geometrical Aspects of Nearest Neighbor Computation in High-Dimensional Data.
Statistical Computing 2024 - 54. Arbeitstagung der Arbeitsgruppen Statistical Computing, Klassifikation und Datenanalyse in den Biowissenschaften. Günzburg, Germany, Jul 28-31, 2024. PDF
[38]
M. Herrmann • F. J. D. Lange • K. Eggensperger • G. Casalicchio • M. Wever • M. Feurer • D. Rügamer • E. Hüllermeier • A.-L. Boulesteix • B. Bischl
Position: Why We Must Rethink Empirical Research in Machine Learning.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Position: Why We Must Rethink Empirical Research in Machine Learning.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
[37]
M. M. Mandl • A. S. Becker-Pennrich • L. C. Hinske • S. Hoffmann • A.-L. Boulesteix
Addressing researcher degrees of freedom through minP adjustment.
BMC Medical Research Methodology 24.152. Jul. 2024. DOI
Addressing researcher degrees of freedom through minP adjustment.
BMC Medical Research Methodology 24.152. Jul. 2024. DOI
[36]
M. Herrmann • D. Kazempour • F. Scheipl • P. Kröger
Enhancing cluster analysis via topological manifold learning.
Data Mining and Knowledge Discovery 38. Apr. 2024. DOI
Enhancing cluster analysis via topological manifold learning.
Data Mining and Knowledge Discovery 38. Apr. 2024. DOI
[35]
G. S. Collins • K. G. M. Moons • P. Dhiman • R. D. Riley • A. L. Beam • B. Van Calster • M. Ghassemi • X. Liu • J. B. Reitsma • M. van Smeden • A.-L. Boulesteix • J. C. Camaradou • L. A. Celi • S. Denaxas • A. K. Denniston • B. Glocker • R. M. Golub • H. Harvey • G. Heinze • M. M. Hoffman • A. P. Kengne • E. Lam • N. Lee • E. W. Loder • L. Maier-Hein • B. A. Mateen • M. D. McCradden • L. Oakden-Rayner • J. Ordish • R. Parnell • S. Rose • K. Singh • L. Wynants • P. Logullo
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.
The BMJ 385.e078378. Apr. 2024. DOI
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.
The BMJ 385.e078378. Apr. 2024. DOI
[34]
M. Wünsch • M. Herrmann • E. Noltenius • M. Mohr • T. P. Morris • A.-L. Boulesteix
On the handling of method failure in comparison studies.
Preprint (Apr. 2024). arXiv
On the handling of method failure in comparison studies.
Preprint (Apr. 2024). arXiv
[33]
M. M. Mandl • S. Hoffmann • S. Bieringer • A. E. Jacob • M. Kraft • S. Lemster • A.-L. Boulesteix
Raising awareness of uncertain choices in empirical data analysis: A teaching concept toward replicable research practices.
PLOS Computational Biology 20.3. Mar. 2024. DOI
Raising awareness of uncertain choices in empirical data analysis: A teaching concept toward replicable research practices.
PLOS Computational Biology 20.3. Mar. 2024. DOI
[32]
C. Sauer • S. Hoffmann • T. Ullmann • A.-L. Boulesteix
Explaining the optimistic performance evaluation of newly proposed methods: A cross-design validation experiment.
Biometrical Journal 66.1. Jan. 2024. DOI
Explaining the optimistic performance evaluation of newly proposed methods: A cross-design validation experiment.
Biometrical Journal 66.1. Jan. 2024. DOI
[31]
B. S. Siepe • F. Bartoš • T. P. Morris • A.-L. Boulesteix • D. W. Heck • S. Pawel
Simulation Studies for Methodological Research in Psychology: A Standardized Template for Planning, Preregistration, and Reporting.
Psychological Methods Advance online publication. Jan. 2024. DOI
Simulation Studies for Methodological Research in Psychology: A Standardized Template for Planning, Preregistration, and Reporting.
Psychological Methods Advance online publication. Jan. 2024. DOI
[30]
Z. S. Dunias • B. Van Calster • D. Timmerman • A.-L. Boulesteix • M. van Smeden
A comparison of hyperparameter tuning procedures for clinical prediction models: A simulation study.
Statistics in Medicine. Jan. 2024. DOI
A comparison of hyperparameter tuning procedures for clinical prediction models: A simulation study.
Statistics in Medicine. Jan. 2024. DOI
[29]
R. Hornung • F. Ludwigs • J. Hagenberg • A.-L. Boulesteix
Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study.
Wiley Interdisciplinary Reviews: Computational Statistics 16.1. Jan. 2024. DOI
Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study.
Wiley Interdisciplinary Reviews: Computational Statistics 16.1. Jan. 2024. DOI
[28]
M. Wünsch • C. Sauer • P. Callahan • L. C. Hinske • A.-L. Boulesteix
From RNA sequencing measurements to the final results: a practical guide to navigating the choices and uncertainties of gene set analysis.
Wiley Interdisciplinary Reviews: Computational Statistics 16.1. Jan. 2024. DOI
From RNA sequencing measurements to the final results: a practical guide to navigating the choices and uncertainties of gene set analysis.
Wiley Interdisciplinary Reviews: Computational Statistics 16.1. Jan. 2024. DOI
2023
[27]
J. Gauss • F. Scheipl • M. Herrmann
DCSI–An improved measure of cluster separability based on separation and connectedness.
Preprint (Oct. 2023). arXiv
DCSI–An improved measure of cluster separability based on separation and connectedness.
Preprint (Oct. 2023). arXiv
[26]
S. Hoffmann • F. Scheipl • A.-L. Boulesteix
Reproduzierbare und replizierbare Forschung.
Moderne Verfahren der Angewandten Statistik. Sep. 2023. DOI
Reproduzierbare und replizierbare Forschung.
Moderne Verfahren der Angewandten Statistik. Sep. 2023. DOI
[25]
I. van Mechelen • A.-L. Boulesteix • R. Dangl • N. Dean • C. Hennig • F. Leisch • D. Steinley • M. J. Warrens
A white paper on good research practices in benchmarking: The case of cluster analysis.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 13.6. Jul. 2023. DOI
A white paper on good research practices in benchmarking: The case of cluster analysis.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 13.6. Jul. 2023. DOI
[24]
M. Herrmann • F. Pfisterer • F. Scheipl
A geometric framework for outlier detection in high-dimensional data.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery e1491. Apr. 2023. DOI
A geometric framework for outlier detection in high-dimensional data.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery e1491. Apr. 2023. DOI
[23]
T. Ullmann • A. Beer • M. Hünemörder • T. Seidl • A.-L. Boulesteix
Over-optimistic evaluation and reporting of novel cluster algorithms: An illustrative study.
Advances in Data Analysis and Classification 17. Mar. 2023. DOI
Over-optimistic evaluation and reporting of novel cluster algorithms: An illustrative study.
Advances in Data Analysis and Classification 17. Mar. 2023. DOI
[22]
B. Bischl • M. Binder • M. Lang • T. Pielok • J. Richter • S. Coors • J. Thomas • T. Ullmann • M. Becker • A.-L. Boulesteix • D. Deng • M. Lindauer
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 13.2. Mar. 2023. DOI
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 13.2. Mar. 2023. DOI
[21]
T. Ullmann • S. Peschel • P. Finger • C. L. Müller • A.-L. Boulesteix
Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering.
PLOS Computational Biology 19.1. Jan. 2023. DOI
Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering.
PLOS Computational Biology 19.1. Jan. 2023. DOI
2022
[20]
T. Ullmann
Evaluation of clustering results and novel cluster algorithms: a metascientific perspective.
Dissertation LMU München. Dec. 2022. DOI
Evaluation of clustering results and novel cluster algorithms: a metascientific perspective.
Dissertation LMU München. Dec. 2022. DOI
[19]
M. Herrmann
Towards more reliable machine learning: conceptual insights and practical approaches for unsupervised manifold learning and supervised benchmark studies.
Dissertation LMU München. Oct. 2022. DOI
Towards more reliable machine learning: conceptual insights and practical approaches for unsupervised manifold learning and supervised benchmark studies.
Dissertation LMU München. Oct. 2022. DOI
[18]
M. van Smeden • G. Heinze • B. Van Calster • F. W. Asselbergs • P. E. Vardas • N. Bruining • P. de Jaegere • J. H. Moore • S. Denaxas • A.-L. Boulesteix • K. G. M. Moons
Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease.
European Heart Journal 43.31. Aug. 2022. DOI
Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease.
European Heart Journal 43.31. Aug. 2022. DOI
[17]
T. Ullmann • C. Hennig • A.-L. Boulesteix
Validation of cluster analysis results on validation data: A systematic framework.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12.3. May. 2022. DOI
Validation of cluster analysis results on validation data: A systematic framework.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12.3. May. 2022. DOI
[16]
C. Sauer • M. Herrmann • C. Wiedemann • G. Casalicchio • A.-L. Boulesteix
Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12.2. Mar. 2022. DOI
Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12.2. Mar. 2022. DOI
2021
[15]
M. Herrmann • F. Scheipl
A Geometric Perspective on Functional Outlier Detection.
Stats 4.4. Nov. 2021. DOI
A Geometric Perspective on Functional Outlier Detection.
Stats 4.4. Nov. 2021. DOI
[14]
H. Seibold • A. Charlton • A.-L. Boulesteix • S. Hoffmann
Statisticians, Roll Up Your Sleeves! There's A Crisis to be Solved.
Significance 18.4. Aug. 2021. DOI
Statisticians, Roll Up Your Sleeves! There's A Crisis to be Solved.
Significance 18.4. Aug. 2021. DOI
[13]
H. Seibold • S. Czerny • S. Decke • R. Dieterle • T. Eder • S. Fohr • N. Hahn • R. Hartmann • C. Heindl • P. Kopper • D. Lepke • V. Loidl • M. M. Mandl • S. Musiol • J. Peter • A. Piehler • E. Rojas • S. Schmid • H. Schmidt • M. Schmoll • L. Schneider • X.-Y. To • V. Tran • A. Völker • M. Wagner • J. Wagner • M. Waize • H. Wecker • R. Yang • S. Zellner • M. Nalenz
A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.
PLOS One 16.6. Jun. 2021. DOI
A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.
PLOS One 16.6. Jun. 2021. DOI
[12]
S. Klau • S. Hoffmann • C. J. Patel • J. P. A. Ioannidis • A.-L. Boulesteix
Examining the robustness of observational associations to model, measurement and sampling uncertainty with the vibration of effects framework.
International Journal of Epidemiology 50.1. Feb. 2021. DOI
Examining the robustness of observational associations to model, measurement and sampling uncertainty with the vibration of effects framework.
International Journal of Epidemiology 50.1. Feb. 2021. DOI
2020
[11]
M. Herrmann • F. Scheipl
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction.
Preprint (Dec. 2020). arXiv
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction.
Preprint (Dec. 2020). arXiv
[10]
A.-L. Boulesteix • S. Hoffmann • A. Charlton • H. Seibold
A replication crisis in methodological research?
Significance 17.5. Oct. 2020. DOI
A replication crisis in methodological research?
Significance 17.5. Oct. 2020. DOI
[9]
M. Herrmann • P. Probst • R. Hornung • V. Jurinovic • A.-L. Boulesteix
Large-scale benchmark study of survival prediction methods using multi-omics data.
Briefings in Bioinformatics. Aug. 2020. DOI
Large-scale benchmark study of survival prediction methods using multi-omics data.
Briefings in Bioinformatics. Aug. 2020. DOI
[8]
N. Ellenbach • A.-L. Boulesteix • B. Bischl • K. Unger • R. Hornung
Improved outcome prediction across data sources through robust parameter tuning.
Journal of Classification 38. Jul. 2020. DOI
Improved outcome prediction across data sources through robust parameter tuning.
Journal of Classification 38. Jul. 2020. DOI
[7]
C. Stachl • Q. Au • R. Schoedel • S. D. Gosling • G. M. Harari • D. Buschek • S. T. Völkel • T. Schuwerk • M. Oldemeier • T. Ullmann • H. Hussmann • B. Bischl • M. Bühner
Predicting personality from patterns of behavior collected with smartphones.
Proceedings of the National Academy of Sciences 117.30. Jul. 2020. DOI
Predicting personality from patterns of behavior collected with smartphones.
Proceedings of the National Academy of Sciences 117.30. Jul. 2020. DOI
[6]
S. Klau • M.-L. Martin-Magniette • A.-L. Boulesteix • S. Hoffmann
Sampling uncertainty versus method uncertainty: a general framework with applications to omics biomarker selection.
Biometrical Journal 62.3. May. 2020. DOI
Sampling uncertainty versus method uncertainty: a general framework with applications to omics biomarker selection.
Biometrical Journal 62.3. May. 2020. DOI
[5]
[4]
2019
[3]
L. M. Weber • W. Saelens • R. Cannoodt • C. Soneson • A. Hapfelmeier • P. P. Gardner • A.-L. Boulesteix • Y. Saeys • M. D. Robinson
Essential guidelines for computational method benchmarking.
Genome Biology 20.125. Jun. 2019. DOI
Essential guidelines for computational method benchmarking.
Genome Biology 20.125. Jun. 2019. DOI
[2]
P. Probst • A.-L. Boulesteix • B. Bischl
Tunability: Importance of Hyperparameters of Machine Learning Algorithms.
Journal of Machine Learning Research 20. Mar. 2019. PDF
Tunability: Importance of Hyperparameters of Machine Learning Algorithms.
Journal of Machine Learning Research 20. Mar. 2019. PDF
[1]
P. Probst • M. N. Wright • A.-L. Boulesteix
Hyperparameters and Tuning Strategies for Random Forest.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9.3. Jan. 2019. DOI
Hyperparameters and Tuning Strategies for Random Forest.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9.3. Jan. 2019. DOI
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