Question
Question 7 (1 point) Consider a decision tree constructed for a classification problem, where the possible classes are A , B and C . How
Question 7 (1 point)
Consider a decision tree constructed for a classification problem, where the possible classes are A, B and C. How many branches does an arbitrary internal node in the decision tree have?
Question 7 options:
1 | |
2 | |
3 | |
4 | |
it varies, depending on what attribute the node branches on |
Question 8 (1 point)
info([a, b]) =
Question 8 options:
entropy(-a, -b) | |
entropy(a, b) | |
entropy(a/(a+b), b/(a+b)) | |
entropy(-a/(a+b), -b/(a+b)) | |
-(a/(a+b))entropy(a/(a+b)) - (b/(a+b))entropy(b/(a+b)) |
Question 9 (1 point)
Consider the process of constructing a decision tree based on the training set in Table 1 and using the information gain algorithm discussed in class. What is the information gain when branching at the root of the decision tree on the attribute Aquatic?
Table 1: Vertebrate Dataset (modified from Introduction to Data Mining by Tan et al.)
Name | Body temp | Give birth | Aquatic | Class |
human | warm | yes | no | mammal |
echidna | warm | no | no | mammal |
salmon | cold | no | yes | fish |
whale | warm | yes | yes | mammal |
eel | cold | no | yes | fish |
bat | warm | yes | no | mammal |
shark | cold | yes | yes | fish |
cat | warm | yes | no | mammal |
Question 9 options:
(1/2)info([4,0]) + (1/2)info([1,3]) | |
info([5,3]) (1/2)info([4,0]) (1/2)info([1,3]) | |
(5/8)info([1,4]) + (3/8)info([3,0]) | |
info([5,3]) (5/8)info([1,4]) (3/8)info([3,0]) | |
none of the above |
Question 10 (1 point)
If we are going to build a decision tree for the dataset in Table 1, which attribute (not considering Name) will be selected as the root?
Table 1: Vertebrate Dataset (modified from Introduction to Data Mining by Tan et al.)
Name | Body temp | Give birth | Aquatic | Class |
human | warm | yes | no | mammal |
echidna | warm | no | no | mammal |
salmon | cold | no | yes | fish |
whale | warm | yes | yes | mammal |
eel | cold | no | yes | fish |
bat | warm | yes | no | mammal |
shark | cold | yes | yes | fish |
cat | warm | yes | no | mammal |
Question 10 options:
Body temp | |
Give birth | |
Aquatic | |
one of Give birth or Aquatic, since it is a tie, randomly pick one | |
any of the three attributes |
Question 11 (1 point)
When the decision tree built from the previous question is used on the test instance in Table 2, what is the predicted class for porcupine?
Table 2: Test Instance
Name | Body temp | Give birth | Aquatic | Class |
porcupine | warm | yes | no | ? |
Question 11 options:
mammal | |
fish | |
cannot decide |
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