2 Classification with Asymmetric Costs As discussed in class, occasionally the standard misclassification error is not a good metric for a given task. In this question, we consider binary classification (Y={0,1}) where the two possible types of errors are not symmetric. Specifically, we associate a cost of p>0 for outputting 0 when the class is 1 , and a cost of q>0 for outputing 1 when the class is 0 . Furthermore, we allow the classifier to output -1, indicating an overall lack of confidence as to the label of the provided example, and incur a cost r>0. Formally, given a classifier f:X{1,0,1} and an example xX with label y{0,1} we define the loss of f with respect to example (x,y) by: (f(x),y)=0pqriff(x)=yiff(x)=0andy=1iff(x)=1andy=0iff(x)=1 (i) Describe a real world scenario where this model of classification may be more appropriate than the standard model seen in class. (ii) Assume that r

p/2 the Bayes classifier is the same as the one we derived in class. Explain intuitively why this makes sense