Building Decision Tree Two step method Tree Construction 1. Pick an attribute for division of given data 2. Divide the given data into sets on the basis of this attribute 3. For every set created above repeat 1 and 2 until you find leaf nodes in all the branches of the tree Terminate Tree Pruning (Optimization) major steps decision tree classification 8. 1 Briefly outline the major steps of decision tree classification. . 8. 2 Why is tree pruning useful in decision tree induction? What is a drawback of using a separate set of tuples to evaluate pruning? 8. 3 Given a decision tree, you have the option of (a) converting the decision tree to rules and then pruning the resulting rules, or (b) pruning the decision tree and then converting the pruned
Chapter 4: Decision Trees Algorithms. Madhu Sanjeevi Lets just first build decision tree for classification problem using above algorithms, similarly we can follow other steps to build the major steps decision tree classification
Decision tree lecture 3. Commercial successor: C5. 0 CART (Classification and Regression Trees) algorithm The generation of binary decision trees Developed by a group of statisticians 6 Basic Algorithm Basic algorithm, [ID3, C4. 5, and CART, (a greedy algorithm) Tree is constructed in a topdown recursive divideand conquer manner At The learning and classification steps of a decision tree are simple and fast. Decision Tree Induction Algorithm. 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. Briefly describe the attribute selection measures for decision tree induction? 6) Explain classification and prediction with and example. 7) Briefly outline the major steps for decision tree classification. major steps decision tree classification Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. A decision node (e. g. , Outlook) has two or more branches (e. g. , Sunny, Overcast and Rainy). Leaf node (e. g. , Play) represents a classification or decision. Figure 6. 1 The data classification process: (a) Learning: Training data are analyzed by a classification algorithm Here the class label attribute is loan decision and the 5. , loandecision, learned model or classifier is represented in the form of classification rules. A Decision Tree, more properly a classification tree, is used to learn a classification function which predicts the value of a dependent attribute (variable) given the values of the independent (input) attributes (variables). This solves a problem known as supervised classification because the dependent attribute and the number of classes (values) that it may have are given. The classification tree method consists of two major steps: Identification of test relevant aspects (so called classifications ) and their corresponding values (called classes ) as well as Combination of different classes from all classifications into test cases.