In this problem, you will use simulation to evaluate (by Monte Carlo) the expected misclassification error rate given a particular generating model. Let be equally divided between classes 0 and 1, and let be normally distributed. Given , . Given , with . The notation just means its a ten-dimensional Gaussian distribution; you can use the "multivariate_normal" function from a random number "rng" to help generate the data. Now, we would like to know the expected test error rate if we fit an SVM to a sample of 50 random training points from class 1 and 50 more from class 0. We can calculate this to high precision by 1) generating a random training sample to train on, 2) evaluating the number of mistakes we make on a large test set, and then 3) repeating (1-2) many times and averaging the error rate for each trial. Aside: in real life we don't know the generating distribution, so we have to use resampling methods instead of the procedure described above. For all of the following, please enter your error rate as a number between zero and 1 (e.g., 0.21 instead of 21 if the error rate is 21%). 9.Py.1 0.0/1.0 point