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Unformatted text preview: CS464 Introduction to Machine Learning 1 Decision Tree Learning Decision tree learning is a method for approximating discretevalued target functions. The learned function is represented by a decision tree . A learned decision tree can also be rerepresented as a set of ifthen rules. Decision tree learning is one of the most widely used and practical methods for inductive inference. It is robust to noisy data and capable of learning disjunctive expressions. Decision tree learning method searches a completely expressive hypothesis . Avoids the difficulties of restricted hypothesis spaces. Its inductive bias is a preference for small trees over large trees. The decision tree algorithms such as ID3, C4.5 are very popular inductive inference algorithms, and they are sucessfully applied to CS464 Introduction to Machine Learning 1 CS464 Introduction to Machine Learning 2 Decision Tree for PlayTennis CS464 Introduction to Machine Learning 2 Outlook Sunny Overcast Rain Humidity High Normal Wind Strong Weak No Yes Yes Yes No CS464 Introduction to Machine Learning 3 Decision Tree Decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances. Each path from the tree root to a leaf corresponds to a conjunction of attribute tests, and The tree itself is a disjunction of these conjunctions. (Outlook = Sunny Humidity = Normal) (Outlook = Overcast) (Outlook = Rain Wind = Weak) Outlook Sunny Overcast Rain Humidity High Normal Wind Strong Weak No Yes Yes Yes No CS464 Introduction to Machine Learning 4 Decision Tree Decision trees classify instances by sorting them down the tree from the root to some leaf node, which provides the classification of the instance. Each node in the tree specifies a test of some attribute of the instance. Each branch descending from a node corresponds to one of the possible values for the attribute. Each leaf node assigns a classification. The instance (Outlook=Sunny, Temperature=Hot, Humidity=High, Wind=Strong) is classified as a negative instance. CS464 Introduction to Machine Learning 4 CS464 Introduction to Machine Learning 5 When to Consider Decision Trees Instances are represented by attributevalue pairs. Fixed set of attributes, and the attributes take a small number of disjoint possible values. The target function has discrete output values. Decision tree learning is appropriate for a boolean classification, but it easily extends to learning functions with more than two possible output values. Disjunctive descriptions may be required. decision trees naturally represent disjunctive expressions....
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This note was uploaded on 12/27/2009 for the course CS 464 taught by Professor Demir during the Fall '08 term at Bilkent University.
 Fall '08
 Demir
 Machine Learning

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