Question

Can anyone give a detailed answer to this, any help would be much appreicated.

Problem 2 (25 points) Consider a feature x that has probability distributions under two competing class labels as given/shown. 1.5 otherwise 0.5 otherwise 0.5 1.5 2.5 (a) (10 points) Given loss values Loss(Deciding wi given truth is wi)-λί,2 0 for i,je {1,2) and class prior probabilities p(wi) and p(/2), express the minimum expected loss decision rule using a discriminant function that is simplified as much as possible. Please show all steps during your simplification process. Also indicate the particular minimum expected loss decision rule corresponding to the case where 0-1 loss is used, and class priors are equal (maximum likelihood classifier) (b) (15 points) Determine analytically and sketch the ROC curve using P(Decision- w2lTruth- wa) for the vertical axis and P(Decision = w| Truth-w1) for the horizontal axis. Indicate the point on the ROC curve that corresponds to the maximum likelihood classifier

Answer 1