Interpreting a confusion matrix for a multiclass classification? I am struggling to understand why the image below classifies the third column of the first row as a true negative (typically true negatives exists on the diagonals of a confusion matrix as it indicates negative samples that were correctly predicted). rather than classifying it as a false negative as it is 'a' samples predicted as a 'c'.


The green row, yellow column seem to indicate that the example is currently focusing on classification of class b versus the rest. The cell to which the arrow points contains the cases which were predicted to be not class b (class c was predicted), and were in fact not class b (actual class was a). These cases are thus correctly classified as not class b: true negatives.
