CECS 451 Fall 2017 Artificial Intelligence Homework 5: Due Dec 1st, 2017 (Submit in class before Lecture) (5 points) Consider the following table of observations: Humidity Windy Play Golf? NO. Outlook Temperature 1 Sunny hot 2 Sunny high false hot overcast hot mild cool cool true false Y falseY false'Y true true Y false N false Y false Y true Y true Y false Y true N 4 rain 5 rain 6 rain 7 overcast cool 8 Ig high high normal normal normal mild cool mild mild ig normal normal normal high normal sunny sunny 10 rain 11 sunny 12 overcast mild 13 overcast hot 14 rain mild From the classified examples in the above table, construct two decision trees (by hand) for the classification "Play Golf." 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 tree. 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 CECS 451 Fall 2017 Artificial Intelligence Homework 5: Due Dec 1st, 2017 (Submit in class before Lecture) (5 points) Consider the following table of observations: Humidity Windy Play Golf? NO. Outlook Temperature 1 Sunny hot 2 Sunny high false hot overcast hot mild cool cool true false Y falseY false'Y true true Y false N false Y false Y true Y true Y false Y true N 4 rain 5 rain 6 rain 7 overcast cool 8 Ig high high normal normal normal mild cool mild mild ig normal normal normal high normal sunny sunny 10 rain 11 sunny 12 overcast mild 13 overcast hot 14 rain mild From the classified examples in the above table, construct two decision trees (by hand) for the classification "Play Golf." 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 tree. 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