Gilbraith, William E.Celani, Caelin P.Booksh, Karl S.2022-07-252022-07-252022-07-05Gilbraith, WE, Celani, CP, Booksh, KS. Visualization of Confusion Matrices with Network Graphs. Journal of Chemometrics. 2022;e3435. https://doi.org/10.1002/cem.34351099-128Xhttps://udspace.udel.edu/handle/19716/31151This is the peer reviewed version of the following article: Gilbraith, WE, Celani, CP, Booksh, KS. Visualization of Confusion Matrices with Network Graphs. Journal of Chemometrics. 2022;e3435. https://doi.org/10.1002/cem.3435, which has been published in final form at https://doi.org/10.1002/cem.3435. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. This article will be embargoed until 07/05/2023.The use of network analysis as a means of visualizing the off-diagonal (misclassified) elements of a confusion matrix is demonstrated and the potential to use the network graphs as a guide for developing hierarchical classification models is presented. A very brief summary of graph theory is described. This is followed by an explanation and code with examples of how these networks can then be used for visualization of confusion matrices. The use of network graphs to provide insight into differing model performance is also addressed.en-USchemometricsnetwork analysisgraph theoryconfusion matrixvisualizationRigraphArticle