How to Implement ID3 Decision Tree Algorithm using JAVA
/ March 28, 2018

The development of Information technology has generated large amount of databases and huge data in various areas. The research in databases and information technology has given rise to an approach to store and manipulate this precious data for further decision making. Decision tree is powerful and popular tool for classification and prediction. Decision trees represent rules. A decision tree is predictive model that, as its name implies, can be viewed as a tree. Specifically each branch of the tree is a classification question and the leaves of the tree are partitions of the dataset with their classification. Decision tree is a classifier in the form of a tree structure, where each node is either: A leaf node- indicates the value of the target attribute(class) of examples, or A decision node- specifies some test to be carried out on a single attribute- value, with one branch and sub-tree for each possible outcome of the test. ID3 algorithm is primarily used for decision making. ID3 (Iterative Dichotomiser 3) algorithm invented by Ross Quinlan is used to generate a decision tree from a dataset. There are different implementations given for Decision Trees. Major ones are ID3: Iternative Dichotomizer was the very first implementation…

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