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A decision tree is a structure that includes a root node, branches, and leaf nodes. Each internal node denotes a test on an attribute, each branch denotes the outcome of a test, and each leaf node holds a class label. The topmost node in the tree is the root node.

The following decision tree is for the concept buy_computer that indicates whether a customer at a company is likely to buy a computer or not. Each internal node represents a test on an attribute. Each leaf node represents a class.

The benefits of having a decision tree are as follows −

- It does not require any domain knowledge.
- It is easy to comprehend.
- The learning and classification steps of a decision tree are simple and fast.

A machine researcher named J. Ross Quinlan in 1980 developed a decision tree algorithm known as ID3 (Iterative Dichotomiser). Later, he presented C4.5, which was the 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 DAlgorithm : Generate_decision_treeInput: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 TreeMethodcreate 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 the training data due to noise or outliers. The pruned trees are smaller and less complex.

There are two approaches to prune a tree −

**Pre-pruning**− The tree is pruned by halting its construction early.**Post-pruning**- This approach removes a sub-tree from a fully grown tree.

The cost complexity is measured by the following two parameters −

- Number of leaves in the tree, and
- Error rate of the tree.

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