CSE 4301/5290 Homework 5 Due: December 7, Wed, 5pm; Submit Server: course = ai , project = hw5 1. Programming (Java/C/C++/LISP): Implement the decision-tree learning algorithm and evaluate the accu-racy of the algorithm on the provided training and test sets. All data sets are available on course web site. The Restaurant data set in Figure 18.3 (3/2Ed) is the training set. No test set for this data set. Your im-plementation should reproduce the tree in Figure 18.6 (3/2Ed). The functions (stated in LISP) include: ; given examples, attributes, and default class ; return a decision tree (defun learn-decision-tree (examples attributes default-class) ...) ; given a tree, print the tree using pre-order traversal ; with more indentation for nodes at deeper levels (defun print-decision-tree (tree) ...) ; given a tree and a data set, return the accuracy (%) of the tree on ; the dataset (defun eval-decision-tree (tree dataset . ..) ...) ; given the file names for attributes, training set, and test set ; read in the attributes, training set, and test set ; learn a decision tree from the training data ; print the decision tree ; print the accuracy of decision tree on the training set
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