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Teaser image to Deriving interpretable thresholds for variable importance in random forests by permutation

 

Deriving interpretable thresholds for variable importance in random forests by permutation

Maria Blanco, Staburo GmbH
Tim Müller, Staburo GmbH
Laura Schlieker, Staburo GmbH
Armin Ott, Staburo GmbH
Hannes Buchner, Staburo GmbH

   10.05.2023

   4:15 pm - 5:45 pm

   LMU Department of Statistics and via zoom

In clinical research, discovering predictive biomarkers is vital for precision medicine. The authors propose a variation of Random Forests, categorizing variables as confirmed, tentative, or rejected. Simulations and real datasets demonstrate its effectiveness in visually presenting multiple criteria.

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