The following shows a history of customers with their incomes, ages and an attribute called "Have_MacBook" indicating whether they have a MacBook. We also indicate whether they will buy a Mac Studio or not in the last column. The first column "No." is just for you to refer the record number only and vou do not need to use this column for generating the classifier. You cannot use XLMiner in this question. (a) We want to train a CART decision tree classifier to predict whether a new customer will buy a Mac Studio or not. We define the value of attribute Buy_MacStudio to be the label of a record. (i) Please find a CART decision tree according to the above example. In the decision tree, whenever we process (1) a node containing at least 85% records with the same label or (2) a node containing at most 3 records, we stop to process this node for splitting. (ii) Consider a new young customer whose income is medium and he has a MacBook. Please predict whether this new customer will buy a Mac Studio or not. (b) What is the difference between the C4.5 decision tree and the ID3 decision tree? Why is there a difference