Anne‐Laure Boulesteix

Bias in random forest variable importance measures: Illustrations, sources and a solution

Carolin Strobl, Anne‐Laure Boulesteix, Achim Zeileis et al. · BMC Bioinformatics · 2007 · 3.6K citations · Full text

Computing, Bioinformatics, Biostatistics +16

Conditional variable importance for random forests

Carolin Strobl, Anne‐Laure Boulesteix, Thomas Kneib et al. · BMC Bioinformatics · 2008 · 3.2K citations · Full text

Computing, Bioinformatics, Biostatistics +10

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Gary S. Collins, Karel G.M. Moons, Paula Dhiman et al. · BMJ · 2024 · 2.2K citations · Full text

Computing, Biostatistics, Health Informatics +17

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Anne‐Laure Boulesteix, Silke Janitza, Jochen Kruppa et al. · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · 2012 · 925 citations · Full text

Computing, Bioinformatics, Biostatistics +9

Random forest versus logistic regression: a large-scale benchmark experiment

Raphaël Couronné, Philipp Probst, Anne‐Laure Boulesteix · BMC Bioinformatics · 2018 · 861 citations · Full text

Computing, Data Science, Clinical Decision Support +14