DECISION TREE ALGORITHM

The above shows the description and execution of a basic algorithm for inducing a decision tree from training tuples. The algorithm follows a top down approach, similar to how decision trees are constructed by algorithms such as ID3, C4.5 and CART. However the illustration only shows the first two levels of the tree. Can someone help me complete the example to show the entire resulting decision tree. Include all nodes, branches and partitions please.

The algorithm is called with three parameters: D, attributelist, and Attribute selection method. We refer to D as a data partition. Initially, it is the complete set of training tuples and their associated class labels. The list of attributes describing the tuples. Attribute.selection.method specifies a heuris- tic procedure for selecting the attribute that "best" discriminates the given tuples according to class. This procedure employs an attribute selection measure such as information gain or the Gini index. Whether the tree is strictly binary is generally driven by the attribute selection measure. Some attribute selection measures, such as the Gini index, enforce the resulting tree to be binary. Others, like information gain, do not, therein allowing multiway splits (i.e., two or more branches to be grown from a node) parameter attribute list is a The tree starts as a single node, N, representing the training tuples in D (step 1).3 The algorithm is called with three parameters: D, attributelist, and Attribute selection method. We refer to D as a data partition. Initially, it is the complete set of training tuples and their associated class labels. The list of attributes describing the tuples. Attribute.selection.method specifies a heuris- tic procedure for selecting the attribute that "best" discriminates the given tuples according to class. This procedure employs an attribute selection measure such as information gain or the Gini index. Whether the tree is strictly binary is generally driven by the attribute selection measure. Some attribute selection measures, such as the Gini index, enforce the resulting tree to be binary. Others, like information gain, do not, therein allowing multiway splits (i.e., two or more branches to be grown from a node) parameter attribute list is a The tree starts as a single node, N, representing the training tuples in D (step 1).3