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B . Generating Classification Rules from a Decision Tree ( 5 0 points ) Given a database table containing weather data as follows: table

B. Generating Classification Rules from a Decision Tree (50 points)
Given a database table containing weather data as follows:
\table[[(0utlook,Temperature,Humidity,Windy,Class: Play],[Sunny,Hot,High,False,No],[Sunny,Hot,High,True,No],[(0vercast,Hot,High,False,Yes],[Rainy,Mild,High,False,Yes],[Rainy,Cool,Normal,False,Yes],[Rainy,Cool,Normal,True,No],[(0vercast,Cool,Normal,True,Yes],[Sunny,Mild,High,False,No],[Sunny,Cool,Normal,False,Yes],[Rainy,Mild,Normal,False,Yes],[Sunny,Mild,Normal,True,Yes],[(1)vercast,Mild,High,True,Yes],[(0)vercast,Hot,Normal,False,Yes],[Rainy,Mild,High,True,No]]
Please use Gini Index and the decision tree learning algorithm to induce a decision tree (binary partition) from the given training samples in the weather database table. (Please provide step by step Gini Index calculation with its corresponding trees).
Please extract the classification rules from the generated decision tree in 1.
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