A probability distribution on the real line is a mixture of two classes +1 and −1, the first one is the uniform distribution on the interval [−1, 1], the second is a normal distribution N(2, 1), with prior probabilities (probability of each class) 0.3 and 0.7 respectively. 1 (a) What it is the Bayes optimal classifier f ∗ ? (b) Compute the classification loss of f ∗ (the Bayes risk). (c) Compute the classification loss of 1-NN classifier. Compare it to the loss of f ∗ . (d) Sample 1000 points from the distribution and compute the loss of f ∗ experimentally. (e) Same for 1-NN and 3-NN (use a separate sample of 1000 points as training data). You can use a k-NN implementation in Matlab or, if you prefer, implement it yourself (in that case show