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A computationally fast variable importance test for random forests for high-dimensional data., , и . Adv. Data Anal. Classif., 12 (4): 885-915 (2018)Unbiased split selection for classification trees based on the Gini Index, , и . Computational Statistics & Data Analysis, 52 (1): 483--501 (15.09.2007)Tunability: Importance of Hyperparameters of Machine Learning Algorithms., , и . J. Mach. Learn. Res., (2019)Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value, , и . Bioinformatics, 24 (15): 1698--1706 (2008)Partial least squares: a versatile tool for the analysis of high-dimensional genomic data, и . Briefings in bioinformatics, 8 (1): 32–44 (2007)Identification of interaction patterns and classification with applications to microarray data., и . Comput. Stat. Data Anal., 50 (3): 783-802 (2006)Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges, , , , , , , , , и 2 other автор(ы). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, (10.03.2023)Funding Information: Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology, Grant/Award Number: BAYERN DIGITAL II; Bundesministerium für Bildung und Forschung, Grant/Award Number: 01IS18036A; Deutsche Forschungsgemeinschaft (Collaborative Research Center), Grant/Award Number: SFB 876‐A3; Federal Statistical Office of Germany; Research Center “Trustworthy Data Science and Security” Funding information Funding Information: The authors of this work take full responsibilities for its content. This work was supported by the Federal Statistical Office of Germany; the Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876, A3; the Research Center “Trustworthy Data Science and Security”, one of the Research Alliance centers within the https://uaruhr.de ; the German Federal Ministry of Education and Research (BMBF) under Grant No. 01IS18036A; and the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics‐Data‐Applications (ADA‐Center) within the framework of “BAYERN DIGITAL II.”.Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data., , , , и . BMC Bioinform., 19 (1): 322:1-322:14 (2018)Cross-study validation for the assessment of prediction algorithms., , , , , , и . Bioinform., 30 (12): 105-112 (2014)Combining clinical and molecular data in regression prediction models: insights from a simulation study., , , , и . Briefings Bioinform., 21 (6): 1904-1919 (2020)