Data Mining - Decision Tree Induction
The decision tree is a structure that includes root node, branch and leaf node. Each internal node denotes a test on attribute, each branch denotes the outcome of test and each leaf node holds the class label. The topmost node in the tree is the root node.
The following decision tree is for concept buy_computer, that indicates whether a customer at a company is likely to buy a computer or not. Each internal node represents the test on the attribute. Each leaf node represents a class.
Advantages of Decision Tree
It does not require any domain knowledge.
It is easy to assimilate by human.
Learning and classification steps of decision tree are simple and fast.
Decision Tree Induction Algorithm
A machine researcher named J. Ross Quinlan in 1980 developed a decision tree algorithm. This Decision Tree Algorithm is known as ID3(Iterative Dichotomiser). Later he gave C4.5 which was successor of ID3. ID3 and C4.5 adopt a greedy approach. In this algorithm there is no backtracking, the trees are constructed in a top down recursive divide-and-conquer manner.
Generating a decision tree form training tuples of data partition D Algorithm : Generate_decision_tree Input: Data partition, D, which is a set of training tuples and their associated class labels. attribute_list, the set of candidate attributes. Attribute selection method, a procedure to determine the splitting criterion that best partitions that the data tuples into individual classes. This criterion includes a splitting_attribute and either a splitting point or splitting subset. Output: A Decision Tree Method create a node N; if tuples in D are all of the same class, C then return N as leaf node labeled with class C; if attribute_list is empty then return N as leaf node with labeled with majority class in D;|| majority voting apply attribute_selection_method(D, attribute_list) to find the best splitting_criterion; label node N with splitting_criterion; if splitting_attribute is discrete-valued and multiway splits allowed then // no restricted to binary trees attribute_list = splitting attribute; // remove splitting attribute for each outcome j of splitting criterion // partition the tuples and grow subtrees for each partition let Dj be the set of data tuples in D satisfying outcome j; // a partition if Dj is empty then attach a leaf labeled with the majority class in D to node N; else attach the node returned by Generate decision tree(Dj, attribute list) to node N; end for return N;
Tree Pruning is performed in order to remove anomalies in training data due to noise or outliers. The pruned trees are smaller and less complex.
Tree Pruning Approaches
Here is the Tree Pruning Approaches listed below:
Prepruning - The tree is pruned by halting its construction early.
Postpruning - This approach removes subtree form fully grown tree.
The cost complexity is measured by following two parameters:
Number of leaves in the tree
Error rate of the tree