Variance of Eval

[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.

[magdon]

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?)]

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.