Here are two useful facts from probability:
The variance of a sum of
independent terms is the sum of the variances:
When you scale a random quantity its variance scales quadratically:
[
Hint: so, if you scale something by
its variance scales by
; the validation error is the average of K independent things (What things? Why are they independent?)]
Quote:
Originally Posted by axelrv
I'm confused about how to simplify expressions involving Var[Eval(g)].
I know that Var[Eval(g)] = E [ ( Eval(g)  E[Eval(g)] )^2] = E [ ( Eval(g)  Eout(g) )^2] and that for classification P[g(x) != y] = Eout(g). I'm not sure how to bring K into any of these expressions.
Any help would be greatly appreciated.
