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COMPARISON OF INDUCTION METHODS FOR DECISION TREE
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Author(s):
VANDNA DAHIYA
Vol - 5, Issue- 11 ,
Page(s) : 141 - 148
(2014 )
DOI : https://doi.org/10.32804/IRJMST
Abstract
Induction of decision trees is the learning from examples. Decision tree is a flowchart like structure where the root node and all the internal nodes represent an attribute with a question and the arcs represent possible outcomes for each question. Class-labeled tuples are used for this learning to design the decision tree for further classification of data in data mining. Goal is to create a model which can predict the class for the tuples. Several methods have been developed for developing the decision tree. This paper tries to compare two of the methods namely, Information Gain and Gain Ratio with their advantages and shortcomings.
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