The following regression tree describes a company's price of its product Y-as a function of two predictors: X1 (Quality) and X2 (Durability) - both are scaled between 0 and 10. The area of regions also represents the percentage of the data points in the training dataset used to build the regions. a. Draw the regression tree for the regions shown in the above graph (2 pts). b. The company has collected new dataset suggesting a low-quality product (X13) can be sold for 6$ if its durability is more than 5(X25). Show the adjusted regression tree (4 pts). c. What is the expected price of each product given these two regression trees - part a and b, and which tree has a higher expected price. (4 pts). For the same decision tree, we study the chance of selling a product given X1 and X2. The number in each region represents the chance of selling the item, P1. d. For which region, the Gini index is minimum and what is its Gini index. (4 pts). e. The company uses Bagging trees to improve its prediction for the sale. Along with the top tree, the company has created two other trees. What is the prediction of sale for an item with a Quality of 6 and Durability of 6 . Show both max voting and probability average results. (4 pts)