Tree-based prediction Name of the dataset: "abalone". Package: "AppliedPredictiveModeling" Source: Data comes from an original (non-machine-learning) study: Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and Wes B Ford (1994) "The Population Biology of Abalone (Haliotis species) in Tasmania. I. Blacklip Abalone (H. rubra) from the North Coast and Islands of Bass Strait", Sea Fisheries Division, Technical Report No. 48 (ISSN 1034-3288) Check http://archive.ics.uci.edu/ml/datasets/Abalone for the details of this dataset.Classification problem Suppose now the variable of interest is Type. Please use what you learned from the classification tree to solve the questions. Questions 5. Under the same training and test parts as Q2 of regression problem, fit a classification tree to the training set using all the other variables as predictors. (1) Plot the fitted tree, which variables are used to construct the tree? (2) Are there any terminal nodes cannot determine the class of the observation? (3) Set the seed to 10, apply the fitted tree to the test set and calculate the misclassification rate. (6pt) 6. Prune the tree by checking the cross-validation errors. Plot the pruned tree and re-calculate the misclassification rate. (4pt) 7. Suppose you are interested in comparing the performance between the linear discriminant analysis and the classification tree, please use the stratified 10-fold cross-validation to calculate the cross-validation errors of these two methods (the cross-validation error here is defined as the average of the misclassification rate). Which method seems to work better? (6pt)