Construct a decision tree given an order of testing the features. Determine the prediction accuracy of a decision tree on a test set. Compute the entropy of a probability distribution. Compute the expected information gain for selecting a feature. Trace the execution of and implement the ID3 algorithm. 2 Examples of Decision Trees

Once a decision tree is learned, it can be used to evaluate new instances to determine their class. The instance is passed down the tree, from the root, until it arrives at a leaf. The class assigned to the instance is the class for the leaf. This procedure is explained by the following pseudocode. Pseudocode to evaluate a decision tree

Tree: Pseudocode 9/8/22 def train(!): store root = tree_recurse(!) def tree_recurse(!!): q = new node() base case –if (SOME CONDITION): recursion –else: q.type= internal find best attribute …

Pseudocode for AVL Balanced Binary Search Tree Methods Balance a sub-tree Note: the following code does not account for empty child sub-trees. You should check for NULL pointers when accessing left or right or height. Primarily, when calculating heights of children. function balance(current) if current == NULL then nnNothing to balance return ...

The pseudo-code for Left-Rotate assumes that right[x] ≠nil[T ], and root’s parent is nil[T ]. Left Rotation on x, makes x the left child of y, and the left subtree of y into the right subtree of x. Pseudocode for Right-Rotate is symmetric: exchange left and right everywhere. Time: O(1) for both Left-Rotate and Right-Rotate,

• The binary search treeT is a decision tree, where the question asked at an internal node v is whether the search key k is less than, equal to, or greater than the key stored at v. • Pseudocode: Algorithm TreeSeach(k, v): Input: A search key k and a node v of a binary search tree T. Ouput: A node w of the subtree T(v) of T rooted at v,

Figure 1 shows the generic pseudo code of decision tree algorithm. ... ... Figure 1 1, Random Forest-CART predicts better than other classification algorithms with 72.73% prediction...

Algorithms for finding consistent trees are efficient for processing large amounts of training data for data mining tasks. Methods developed for handling noisy training data (both class and feature noise). • Methods developed for handling missing feature values. • Recursively build a tree top-down by divide and conquer.

Returns a tree that correctly classifies the given examples. Assume that the targetAttribute, which is the attribute whose value is to be predicted by the tree, is a class variable.

tree = new Tree(root=A) for value in A.values(): exs = examples[e.A == value] subtree = decisionTreeLearning(exs, attributes.remove(A), examples) # note implementation should probably wrap the trivial case returns into trees for consistency: tree.addSubtreeAsBranch(subtree, label=(A, value) return tree