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Write a program that learns a decision tree with the ID3
Added on -2019-09-18
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Write a program that learns a decision tree with the ID3 algorithm. Use your program with the given training data (see the cardata on our website). What is your result tree? How are the instances distributed in the leaf nodes? Output your learned tree and the distribution of instances in the nodes in an XML-format similar to the one shown below. The solution must be sent until 08:00 am on Monday the 21st of November 2016.<?xml version="1.0" encoding="UTF-8" standalone="no"?> <tree classes="class1:123,class2:234,class3:345" entropy="0.123"><node classes="class1:23,class2:123" entropy="0.234" attr1="value1"><node classes="class1:23" entropy="0.0" attr2="value1">class1</node><node classes="class2:123" entropy="0.0" attr2="value2">class2</node></node> <node classes="class1:34,class2:45,class3:56" entropy="0.345" attr1="value2"> ...</node>...</tree>You can see the car data in the attachment.
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