Area under the ROC Curve has the most consistent evaluation for binary classification

• Published in: PloS one Vol. 19; no. 12; p. e0316019
• Main Author: Li, Jing
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.12.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Area Under Curve; Biology and Life Sciences; Classification; Computer and Information Sciences; Data analysis; Data Interpretation, Statistical; Datasets; Evaluation; Evaluation Studies as Topic; Machine Learning; Medicine and Health Sciences; Models, Statistical; Physical Sciences; Ranking; Research and Analysis Methods; ROC Curve; Sample Size; Simulation; Social Sciences; Thresholds; Variance; Variance analysis; United States

The proper use of model evaluation metrics is important for model evaluation and model selection in binary classification tasks. This study investigates how consistent different metrics are at evaluating models across data of different prevalence while the relationships between different variables a...