Decision Trees

Name	Decision Trees
Description	Decision trees are special classifiers. Each decision tree is a tree with a test associated to each inner node. Tests are mappings from the set of all instances of the given example language to the set of successors of the actual node. a fixed element of a finite set of possible categories associated to each leaf. The example language is a form of attribute-value representation. Nominal (and therefore finite) attribute values are supported as well as numerical domains. Figure 1 shows a toy example for boolean tests. Figure 1: Decision tree for a striker in football, playing the ball. In the case of nominal attribute values the tests of each node are usually of the following form: All nodes are marked by the name of an attribute. For each possible value of the attribute there is exactly one successor in the tree. The edge leading to a successor is marked with the related attribute value. In the case of numerical values the set of possible tests has to be adjusted. One possibility is to use tests that check if an attribute value is lower than a constant or not. In this case there have to be two successors in the tree, one for each possible answer. The classification of an instance of the example language works like this: Start with the root node. As long as you don´t have reached a leaf node, chose the successor that is given by the test of the actual node. In the case of nominal attribute values follow the edge marked with the instances value of the attribute whose name is found at the actual node. If you have reached a leaf node, classify the instance by the class associated with the leaf.
Methods	CART ID3 Incremental decision tree learning Top-Down Induction of Decision Trees (TDIDT)