Zobrazeno 1 - 10
of 593
pro vyhledávání: '"Boulesteix, Anne‐Laure"'
Autor:
Schulz-Kümpel, Hannah, Fischer, Sebastian, Nagler, Thomas, Boulesteix, Anne-Laure, Bischl, Bernd, Hornung, Roman
When assessing the quality of prediction models in machine learning, confidence intervals (CIs) for the generalization error, which measures predictive performance, are a crucial tool. Luckily, there exist many methods for computing such CIs and new
Externí odkaz:
http://arxiv.org/abs/2409.18836
Autor:
Wünsch, Milena, Herrmann, Moritz, Noltenius, Elisa, Mohr, Mattia, Morris, Tim P., Boulesteix, Anne-Laure
Comparison studies in methodological research are intended to compare methods in an evidence-based manner, offering guidance to data analysts to select a suitable method for their application. To provide trustworthy evidence, they must be carefully d
Externí odkaz:
http://arxiv.org/abs/2408.11594
Autor:
Herrmann, Moritz, Lange, F. Julian D., Eggensperger, Katharina, Casalicchio, Giuseppe, Wever, Marcel, Feurer, Matthias, Rügamer, David, Hüllermeier, Eyke, Boulesteix, Anne-Laure, Bischl, Bernd
We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to undermine progress in the field. To overcome this alarming situation, we
Externí odkaz:
http://arxiv.org/abs/2405.02200
Publikováno v:
Diagn Progn Res 8, 14 (2024)
Random forests have become popular for clinical risk prediction modelling. In a case study on predicting ovarian malignancy, we observed training c-statistics close to 1. Although this suggests overfitting, performance was competitive on test data. W
Externí odkaz:
http://arxiv.org/abs/2402.18612
Autor:
Wünsch, Milena, Sauer, Christina, Herrmann, Moritz, Hinske, Ludwig Christian, Boulesteix, Anne-Laure
Gene set analysis, a popular approach for analysing high-throughput gene expression data, aims to identify sets of genes that show enriched expression patterns between two conditions. In addition to the multitude of methods available for this task, u
Externí odkaz:
http://arxiv.org/abs/2402.00754
Autor:
Mandl, Maximilian M, Becker-Pennrich, Andrea S, Hinske, Ludwig C, Hoffmann, Sabine, Boulesteix, Anne-Laure
When different researchers study the same research question using the same dataset they may obtain different and potentially even conflicting results. This is because there is often substantial flexibility in researchers' analytical choices, an issue
Externí odkaz:
http://arxiv.org/abs/2401.11537
Autor:
Hornung, Roman, Nalenz, Malte, Schneider, Lennart, Bender, Andreas, Bothmann, Ludwig, Bischl, Bernd, Augustin, Thomas, Boulesteix, Anne-Laure
Estimating the generalization error (GE) of machine learning models is fundamental, with resampling methods being the most common approach. However, in non-standard settings, particularly those where observations are not independently and identically
Externí odkaz:
http://arxiv.org/abs/2310.15108
Autor:
Wünsch, Milena, Sauer, Christina, Callahan, Patrick, Hinske, Ludwig Christian, Boulesteix, Anne-Laure
Gene set analysis, a popular approach for analyzing high-throughput gene expression data, aims to identify sets of related genes that show significantly enriched or depleted expression patterns between different conditions. In the last years, a multi
Externí odkaz:
http://arxiv.org/abs/2308.15171
As the availability of omics data has increased in the last few years, more multi-omics data have been generated, that is, high-dimensional molecular data consisting of several types such as genomic, transcriptomic, or proteomic data, all obtained fr
Externí odkaz:
http://arxiv.org/abs/2302.03991
Autor:
Bové, Daniel Sabanés, Seibold, Heidi, Boulesteix, Anne-Laure, Manitz, Juliane, Gasparini, Alessandro, Guünhan, Burak K., Boix, Oliver, Schuüler, Armin, Fillinger, Sven, Nahnsen, Sven, Jacob, Anna E., Jaki, Thomas
Programming is ubiquitous in applied biostatistics; adopting software engineering skills will help biostatisticians do a better job. To explain this, we start by highlighting key challenges for software development and application in biostatistics. S
Externí odkaz:
http://arxiv.org/abs/2301.11791