Decision tree problem

(30 pts) Consider the following table of observations No. Outlook Temperature Humidity Windy Play Golf? 1 sunny hot 2 sunny hot 3 overcast hot 4 rain mild 5 rain cool 6 rain cool 7 overcast cool 8 sunny mild 9 sunny cool 10 rain 11 sunny mild 12 overcast mild 13 overcast hot 14 rain mild high false N high high false Y high false Y normal fa Y normal true N normal true Y nig normal false Y norma false Y normal true Y high normal false Y high true N true N false N true Y From the classified examples in the above table, construct two decision trees (by hand) for the classification "Play Golf." (a) For the first tree, use Temperature as the root node (this is a really bad choice.) Continue the construction of tree as discussed in class for the subsequent nodes using information gain. Remember that different attributes can be used in different branches on a given level of the tree. (b) For the second tree, follow the Decision Tree Learning algorithm described in class At each step, choose the attribute with the highest information gain. Work out the computations of information gain by hand and draw the decision tree (30 pts) Consider the following table of observations No. Outlook Temperature Humidity Windy Play Golf? 1 sunny hot 2 sunny hot 3 overcast hot 4 rain mild 5 rain cool 6 rain cool 7 overcast cool 8 sunny mild 9 sunny cool 10 rain 11 sunny mild 12 overcast mild 13 overcast hot 14 rain mild high false N high high false Y high false Y normal fa Y normal true N normal true Y nig normal false Y norma false Y normal true Y high normal false Y high true N true N false N true Y From the classified examples in the above table, construct two decision trees (by hand) for the classification "Play Golf." (a) For the first tree, use Temperature as the root node (this is a really bad choice.) Continue the construction of tree as discussed in class for the subsequent nodes using information gain. Remember that different attributes can be used in different branches on a given level of the tree. (b) For the second tree, follow the Decision Tree Learning algorithm described in class At each step, choose the attribute with the highest information gain. Work out the computations of information gain by hand and draw the decision tree