Lötsch, J. und Mayer, B. (2022) A biomedical case study showing that tuning random forests can fundamentally change the interpretation of supervised data structure exploration aimed at knowledge discovery [cd], Bio Med Informatics. Basel: MDPI. doi:10.3390/biomedinformatics2040034.
Chicago Manual of Style 17th edition (full note)Lötsch, Jörn, und Benjamin Mayer. A biomedical case study showing that tuning random forests can fundamentally change the interpretation of supervised data structure exploration aimed at knowledge discovery. Cd. Bio Med Informatics. Basel: MDPI, [2022?], Basel: MDPI, [2022?]. https://doi.org/10.3390/biomedinformatics2040034.
American Psychological Association 7th editionLötsch, J., & Mayer, B. (ca. 2022). A biomedical case study showing that tuning random forests can fundamentally change the interpretation of supervised data structure exploration aimed at knowledge discovery [Cd]. In Bio Med Informatics. MDPI. https://doi.org/10.3390/biomedinformatics2040034
Modern Language Association 9th editionLötsch, J., und B. Mayer. „A biomedical case study showing that tuning random forests can fundamentally change the interpretation of supervised data structure exploration aimed at knowledge discovery“. Bio Med Informatics, cd, MDPI, 2022, https://doi.org/10.3390/biomedinformatics2040034.
ISO-690 (author-date, Deutsch)LÖTSCH, Jörn und Benjamin MAYER, 2022. A biomedical case study showing that tuning random forests can fundamentally change the interpretation of supervised data structure exploration aimed at knowledge discovery. Basel: MDPI