On the Normalization of Confusion Matrices: Methods and Geometric Interpretations

ArXi:2509.04959v2 Announce Type: replace The confusion matrix is a standard tool for evaluating classifiers by providing insights into class-level errors. In heterogeneous settings, its values are shaped by two main factors: class similarity -- how easily the model confuses two classes -- and distribution bias, arising from skewed distributions in the