Artificial Intelligence MCQs with Answers
Practice important Artificial Intelligence MCQs with answers and explanations.
Multiple Choice Questions
Q1171: Entropy measures what in a dataset?
- A: Speed of computation
- B: Purity/impurity of examples
- C: Size of dataset
- D: Training time
View Answer
B
Q1172: What is the value of entropy when all examples belong to one class?
- A: 1
- B: 0.5
- C: Undefined
- D: 0
View Answer
D
Q1173: In entropy calculation, what is the value of log2(1)?
- A: 1
- B: 2
- C: 0
- D: -1
View Answer
C
Q1174: Which of the following represents a correct entropy formula?
- A: Entropy(S) = p+ - p-
- B: Entropy(S) = - p+log2 p+ - p-log2 p-
- C: Entropy(S) = (p+ + p-)log2(p+ / p-)
- D: Entropy(S) = p+ * p-
View Answer
B
Q1175: When does ID3 stop splitting further in decision tree construction?
- A: When gain is zero
- B: When max height is reached
- C: When entropy is 1
- D: When attributes are exhausted and leaf nodes are pure
View Answer
D
Q1176: What is the purpose of Information Gain in ID3?
- A: Reduce size of tree
- B: Reduce entropy
- C: Count attributes
- D: Sort attributes by name
View Answer
B
Q1177: Information Gain is calculated using which metric?
- A: Speed of learning
- B: Entropy reduction
- C: Dataset size
- D: Root path length
View Answer
B
Q1178: Which attribute was selected as the root in the given example?
- A: A
- B: B
- C: E
- D: C
View Answer
A
Q1179: What was the information gain of attribute A in the given example?
- A: 0.02
- B: 0.97
- C: 0.57
- D: 1
View Answer
C
Q1180: What was the entropy of the full training set S in the example?
- A: 1
- B: 0.57
- C: 0.97
- D: 0
View Answer
C