We have a 1-dimensional data set with 10 records, where x is the attribute and y is the target class. Attribute x takes values with 0.1 increments, from 0.1 to 1. Target class y takes two possible values, -1 and 1. Suppose we are applying the AdaBoost algorithm to create an Ensemble classifier that predicts a value for y, when inputted a value of x. The algorithm runs for 5 rounds and generates base classifiers C, C2, C3, C4, C5 with error rates of 0.04, 0.02, 0.01, 0.001, 0.00001, respectively. Corresponding classification results for each round are given in the following table: Round 1 2 3 4 5 x = .1 x = .2 -1 -1 1 1 -1 -1 -1 -1 -1 -1 x = .3 x = .4 -1 -1 -1 1 -1 1 -1 -1 1 -1 x = .5 -1 1 -1 -1 1 x = .6 -1 1 1 -1 1 x = .7 x = 1 -1 1 1 1 1 -1 -1 -1 1 x = .9 -1 1 1 1 -1 x = 1 -1 1 1 1 1 = What would be the predictions of the resulting ensemble classifier for a test set of x {0.3, 0.5, 0.7, 0.9}? Show all your calculations.