b) Assume that you are given the set of labeled training examples below, where each attribute has three possible values

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b) Assume that you are given the set of labeled training examples below, where each attribute has three possible values

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B Assume That You Are Given The Set Of Labeled Training Examples Below Where Each Attribute Has Three Possible Values 1
B Assume That You Are Given The Set Of Labeled Training Examples Below Where Each Attribute Has Three Possible Values 1 (34.52 KiB) Viewed 15 times
b) Assume that you are given the set of labeled training examples below, where each attribute has three possible values X, Y, or Z. You choose to learn a decision tree and select "0" as the default output if there are ties. Example A1 A2 A3 Output X X # 1 X 1 #2 Y Y X 1 # 3 X Y X 1 #4 Y X Z 1 X X Z 0 Y Y Y X Y Y 0 Y Y Y 0 Which attribute would choose as the root of the tree? Support your answer with the information gain theory. [8 marks] c) Draw the decision tree using the result(s) from 2(b). Based on the decision tree, what will be the output for a new Example #9 if A1=X, A2=Y A3=Z? [7 marks] d) If all attributes in example (b) are binary, what is the maximum number of decision trees that we can have for this data? [2 marks] # 5 #6 #7 #8 0
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