I am trying to represent graphically the results of a classifier with multiple groups. I am not so much interested in the accuracy of the classifier, as the degree to which the classifier confuses certain categories. So, for example, from a confusion matrix like the following:
a b c a 10 0 6 b 1 8 1 c 7 0 15
The plot show should that
c are more similar than
b or than
c. What I am doing is applying PCA directly to the confusion matrix and then plotting the first and second components. Also creating a dendogram with
-cor(m) as the distance. Both seem to produce the 'right' result but I am not sure this is justified. Also, nonmetric MDS seems to work very much like the PCA approach.
Are these approaches valid? are there better alternatives?