Performance is ranked using PR–AUC, along with ROC–AUC, accuracy, precision, recall, and F1-score. In addition to overall performance, the analysis examines how methods behave across metrics and datasets. The results provide guidance for selecting suitable balancing methods in imbalanced genomic classification tasks related to autism.

Performance evaluation relies on PR-AUC and ROC-AUC metrics, as well as accuracy, precision, recall, and F1-score measurements. Beyond examining general effectiveness, the study investigates how different approaches perform across various metrics and datasets. These findings offer recommendations for choosing appropriate balancing techniques when working with imbalanced genomic classification problems in autism research.