Based on the GrowTree and BestSplit-Class algorithms and using the entropy impurity function, create a decision tree to learn the GoodMovie concept. Show how each node is decided (based on comparing impurity measures), then draw the full decision tree. If there are ties in impurity measures, give higher priority to attributes and values according to their order on page 1: that is, Budget is the highest priority feature and Director is the lowest priority; within Budget, Low is the highest priority value, followed by Medium and then High. [Normally these might be randomly chosen, but we'll use this "inductive bias."] If there are ties to determine a leaf's label, give priority to "Yes." Now apply this learned concept (decision tree) to the three test data sets posted on the Assignments page, and list the correctly and incorrectly classified examples (by number), for each test data set. What is the error rate of the decision tree on the training data? On each test data set?