# Which test should be run on this confusion table data?

I have several confusion tables calculated, see, e.g., table 2 and table 1, but typically I'll have 10 or so. These confusion tables are the result of different classification processes (in fact they are the result of different sensor locations). The row and column labels are the same in each table, only the values and thus the accuracy (the last column) differs.

Now I want to run a test which can tell me which table performs better (i.e, which sensor location is best). I thought of just taking the average of the accuracy, but I would also like to know which rows attribute the most to the difference.

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Perhaps you can clarify a bit more. Are you interested in the type of errors? Are the frequencies in the various categories equal. What do you mean by "which rows attribute the most to the difference"? Does this mean you are interested in testing the difference between rows? Thanks. –  Joel W. Aug 2 '12 at 23:39