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Exercise 8 Consider a binary classification problem in which each observation en is known to belong to one of two classes, corresponding to tn = 0 and tn = 1, and suppose that the procedure for collecting training data is imperfect, so that training points are sometimes mislabelled. For every data point In, instead of having a value of t for the class label, we have instead a value Tin representing the probability that tn = 1. Given a probabilistic model p(t = 10) write down the log likelihood function appropriate to such a data set. Exercise 8 Consider a binary classification problem in which each observation en is known to belong to one of two classes, corresponding to tn = 0 and tn = 1, and suppose that the procedure for collecting training data is imperfect, so that training points are sometimes mislabelled. For every data point In, instead of having a value of t for the class label, we have instead a value Tin representing the probability that tn = 1. Given a probabilistic model p(t = 10) write down the log likelihood function appropriate to such a data set