_Description: ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The example has several 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.For more details visit:http://en.wikipedia.org/wiki/ID3_algorithm http://www.cise.ufl.edu/~ddd/cap6635/Fall-97/Short-papers/2.htm _
How do I, as a developer, start working on the project?
- What dependencies does it have (where are they expressed) and how do I install them? A. _It doesn't have any dependencies.
- How can I see the project working before I change anything? A. _Just run run the code in Eclipse.
- Required Eclipse with jdk 1.6 or higher
- _Dataset is first argument in our program.Dataset is our training data.Fist element of first line is number of records and second element in first line is attribute of each record.Here I sat last attribute as decision attribute. _
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It is second argument in our program.Partition file gives real arrangement of data in file._
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Output gives us new partition file.
- Email : parthtrivedi2492@gmail.com
- "Please open github issues, emails can get messy"
MIT License