1. Least squares classification with regularization. The file Isq_classifier_data. ipynb contains fea- ture n-vectors 21,..., w, and the associated binary labels, y1, . . .,yN, each of which is either +1 or -1. The feature vectors are stored as an n x / matrix X with columns 21, .. ., IN, and the labels are stored as an N-vector y. We will evaluate the error rate on the (training) data X, y and (to check if the model generalizes) a test set Xtest, ytest, also given in Isq classifier_data. ipynb. (a) (10 points) Least squares classifier. Find B, v that N minimize [(x B+0-yi)2 i=1 on the training set. Our predictions are then f(x) = sign(x/ + v). Report the classification error on the training and test sets, the fraction of examples where f(r;) * y;. There is no need to report the B, v values