08-01-2014, 12:53 PM
ID3 and CART Algorithm: A Survey
Abstract
This paper reviewed the ID3 and CART algorithm. ID3 builds a decision tree from a fixed set of examples. The example has many attributes and belongs to a class (like yes or no). The leaf nodes of the decision tree contain the class name whereas a non-leaf node is a decision node. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node. The advantage of learning a decision tree is that a program, rather than a knowledge engineer, elicits knowledge from an expert.
Abstract
This paper reviewed the ID3 and CART algorithm. ID3 builds a decision tree from a fixed set of examples. The example has many attributes and belongs to a class (like yes or no). The leaf nodes of the decision tree contain the class name whereas a non-leaf node is a decision node. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node. The advantage of learning a decision tree is that a program, rather than a knowledge engineer, elicits knowledge from an expert.