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Most algorithms that have been developed for learning decision trees are variations on a core algorithm that employs a top-down, greedy search through the space of possible decision trees This approach is exemplified by the ID3 algorithm (Quinlan 1986) and its successor C45 (Quinlan 1993), which form the primary focus of our discussion here In this section we present the basic algorithm for decision tree learning, corresponding approximately to the ID3 algorithm In Section 37 we consider a number of extensions to this basic algorithm, including extensions incorporated into C45 and other more recent algorithms for decision tree learning Our basic algorithm, ID3, learns decision trees by constructing them topdown, beginning with the question "which attribute should be tested at the root of the tree 'To answer this question, each instance attribute is evaluated using a statistical test to determine how well it alone classifies the training examples The best attribute is selected and used as the test at the root node of the tree A descendant of the root node is then created for each possible value of this attribute, and the training examples are sorted to the appropriate descendant node (ie, down the branch corresponding to the example's value for this attribute) The entire process is then repeated using the training examples associated with each descendant node to select the best attribute to test at that point in the tree This forms a greedy search for an acceptable decision tree, in which the algorithm never backtracks to reconsider earlier choices A simplified version of the algorithm, specialized to learning boolean-valued functions (ie, concept learning), is described in Table 31

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A big studio has experience creating hundreds of titles They have a large staff with experience in what works and what doesn t They have the financial wherewithal to stay in business, and can eat cost overruns if something

Figure 6-2

The central choice in the ID3 algorithm is selecting which attribute to test at each node in the tree We would like to select the attribute that is most useful for classifying examples What is a good quantitative measure of the worth of an attribute We will define a statistical property, called informution gain, that measures how well a given attribute separates the training examples according to their target classification ID3 uses this information gain measure to select among the candidate attributes at each step while growing the tree

12

class in the First View Controller Identity Inspector (Figure 6-3)

.

(Figure 6-4)

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In order to define information gain precisely, we begin by defining a measure commonly used in information theory, called entropy, that characterizes the (im)purity of an arbitrary collection of examples Given a collection S, containing positive and negative examples of some target concept, the entropy of S relative to this boolean classification is

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