Tuning model parameters in class‐imbalanced learning with precision‐recall curve

• Published in: Biometrical journal Vol. 61; no. 3; pp. 652 - 664
• Main Authors: Fu, Guang‐Hui, Yi, Lun‐Zhao, Pan, Jianxin
• Format: Journal Article
• Language: English
• Published: Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.05.2019
• Subjects: Algorithms; Biometry - methods; Brain Injuries, Traumatic - diagnosis; Brain Injuries, Traumatic - metabolism; class imbalance; Colonic Neoplasms - diagnosis; Colonic Neoplasms - genetics; Colonic Neoplasms - physiopathology; Computer simulation; Humans; Learning; Machine Learning; Mathematical models; measurement; Models, Statistical; parameter tuning; Parameters; Performance measurement; precision‐recall curve; Recall; receiver operating characteristic; ROC Curve; Support Vector Machine; Tuning; class imbalance; parameter tuning; precision-recall curve; receiver operating characteristic; measurement

An issue for class‐imbalanced learning is what assessment metric should be employed. So far, precision‐recall curve (PRC) as a metric is rarely used in practice as compared with its alternative of receiver operating characteristic (ROC). This study investigates the performance of PRC as the evaluati...