Hiroyuki Akamine, Akiyuki Uzawa, Satoshi Kuwabara, Shigeaki Suzuki, Yosuke Onishi, Manato Yasuda, Yukiko Ozawa, Naoki Kawaguchi, Tomoya Kubota, Masanori P. Takahashi, Yasushi Suzuki, Genya Watanabe, Takashi Kimura, Takamichi Sugimoto, Makoto Samukawa, Naoya Minami, Masayuki Masuda, Shingo Konno, Yuriko Nagane, & Kimiaki Utsugisawa. (2025). ROC curves for machine learning model performance on training data. https://doi.org/10.1371/journal.pone.0330044.s001
ISO-690 (author-date, English)HIROYUKI AKAMINE, AKIYUKI UZAWA, SATOSHI KUWABARA, SHIGEAKI SUZUKI, YOSUKE ONISHI, MANATO YASUDA, YUKIKO OZAWA, NAOKI KAWAGUCHI, TOMOYA KUBOTA, MASANORI P. TAKAHASHI, YASUSHI SUZUKI, GENYA WATANABE, TAKASHI KIMURA, TAKAMICHI SUGIMOTO, MAKOTO SAMUKAWA, NAOYA MINAMI, MASAYUKI MASUDA, SHINGO KONNO, YURIKO NAGANE und KIMIAKI UTSUGISAWA, 2025. ROC curves for machine learning model performance on training data. . 1 Januar 2025. DOI 10.1371/journal.pone.0330044.s001.
Modern Language Association 9th editionHiroyuki Akamine, Akiyuki Uzawa, Satoshi Kuwabara, Shigeaki Suzuki, Yosuke Onishi, Manato Yasuda, Yukiko Ozawa, Naoki Kawaguchi, Tomoya Kubota, Masanori P. Takahashi, Yasushi Suzuki, Genya Watanabe, Takashi Kimura, Takamichi Sugimoto, Makoto Samukawa, Naoya Minami, Masayuki Masuda, Shingo Konno, Yuriko Nagane, und Kimiaki Utsugisawa. ROC Curves for Machine Learning Model Performance on Training Data. Januar 2025, https://doi.org/10.1371/journal.pone.0330044.s001.
Mohr Siebeck - Recht (Deutsch - Österreich)Hiroyuki Akamine/Akiyuki Uzawa/Satoshi Kuwabara/Shigeaki Suzuki/Yosuke Onishi/Manato Yasuda u. a.: ROC curves for machine learning model performance on training data., 2025,
Emerald - HarvardHiroyuki Akamine, Akiyuki Uzawa, Satoshi Kuwabara, Shigeaki Suzuki, Yosuke Onishi, Manato Yasuda, Yukiko Ozawa, Naoki Kawaguchi, Tomoya Kubota, Masanori P. Takahashi, Yasushi Suzuki, Genya Watanabe, Takashi Kimura, Takamichi Sugimoto, Makoto Samukawa, Naoya Minami, Masayuki Masuda, Shingo Konno, Yuriko Nagane und Kimiaki Utsugisawa. (2025), „ROC curves for machine learning model performance on training data.“, verfügbar unter:https://doi.org/10.1371/journal.pone.0330044.s001.