🚀 go-pugleaf

Suppose I have a classifier that classifies objects into one of n 
classes (populations).  I train the classifier with a "train set."  And to 
test the prediction accuracy of the classifier I predict on a "predict set."
I am wondering: what are some of the better ways to score the
prediction accuracy of my classifier?  All classifications & 
misclassifications have equal cost. 

Here the ways that I'm aware of:

1) (c1 + c2 + ... + cn)/total no. of predictions made
   where cn is the no. of correct predictions made in class n


2) sample correlation coefficient which gives the correlation between
   the prediction and the actual.  the coefficient is always between
   -1 and +1.  There will be a coefficient for each class.  this
   coefficient takes into account type 1 and type 2 errors.


What other popular ways are there to score the prediction accuracy???  
I think I saw someone using the mutual information fuction between the 
predicted and the actual to score the prediction accuracy.  Would this work? 

I would like to know other ways and some of the problems associated with 
them, including the ones I've listed above.  Also, I'm not sure when I can 
assume a certain distribution in my n populations (for example, a normal 
dist.)?          

---sb