3. In some cases of probability-based classification, one wants to classify on minimizing the risk of misclassification. For example, the misclassification of someone sick being healthy should always be avoided, while sometimes, the risk of misclassifying some healthy persons as sick is also high, as the treatment may have significant side effect. The risk of misclassification can be represented by a loss matrix that specifies the risk of misclassifying class Ci as class Gj. Typically, the leading diagonal of loss matrix contains zeros, since there should be no risk for correct prediction. Once the risk matrix is given, we can just extend our classier to Output the result with the minimal risk, which multiplies the probability of each outcome by their respective loss number. Given the loss matrix for a classification of a putative liver disease in three classes: {Healthy, Cirrhosis, Liver Cancer) as below: Real H LC Prediction H 0 5 20 1 0 10 LC 5 3 0 For one patient, the prediction probabilities are P(H)=0.5, P(C)=0.2, P(LC)=0.3. What should be output of the prediction with the minimum risk? Explain your