Zobrazeno 1 - 10
of 2 355
pro vyhledávání: '"Boulesteix A"'
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:
Bagley, Nicholas, Wehbi, Sahar, Mansuryan, Tigran, Boulesteix, Rémy, Maître, Alexandre, Lobato, Yago Arosa, Ferraro, Mario, Mangini, Fabio, Sun, Yifan, Krupa, Katarzyna, Wetzel, Benjamin, Couderc, Vincent, Wabnitz, Stefan, Aceves, Alejandro, Tonello, Alessandro
A coherent concatenation of multiple Townes solitons may lead to a stable infrared and visible broadband filament in ceramic YAG polycrystal. This self-trapped soliton train helps implement self-referenced multiplex coherent anti-Stokes Raman scatter
Externí odkaz:
http://arxiv.org/abs/2409.17040
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
Publikováno v:
Diagnostic and Prognostic Research, Vol 8, Iss 1, Pp 1-14 (2024)
Abstract Background Random forests have become popular for clinical risk prediction modeling. In a case study on predicting ovarian malignancy, we observed training AUCs close to 1. Although this suggests overfitting, performance was competitive on t
Externí odkaz:
https://doaj.org/article/e0a3450ab9244d92851909742ecf816b
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