🚀 go-pugleaf

In article <batra.712560431@beagle>, batra@boulder.Colorado.EDU (sajeev batra) writes:
|> Suppose I have a classifier that classifies objects into one of n 
|> classes (populations).
|> 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 always like to use the following to measure the quality of classification
of a given network:

	o efficiency for class i: this tells you how many of the patterns of
class i were classified as class i elements. So we take

			(no. of i-elements classified as i-elements)
		eff(i)= -------------------------------------------
				(total no. of i-elements)

	o purity for class i: this tells you how pure your 'i-element' sample is. We define it as 

			(no. of i-elements classified as i-elements)
		pur(i)= -------------------------------------------
			(no. of elements classified as i-elements)

Frank Block