Practice Library
All MCQs
Browse exam-wise, subject-wise, and country-wise MCQs with explanations.
Choose an option to check your answer.
A.
Transactions containing X often also contain Y
B.
X occurs in most transactions necessarily
C.
Y causes X
D.
X and Y are negatively correlated
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Correct Answer: A. Transactions containing X often also contain Y
Explanation:
Confidence focuses on the conditional frequency of Y among X transactions.
Choose an option to check your answer.
A.
support(X) divided by support(Y)
B.
support(Y) minus support(X)
C.
support(X ∩ Y) divided by all items
D.
support(X ∪ Y) divided by support(X)
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Correct Answer: D. support(X ∪ Y) divided by support(X)
Explanation:
Confidence estimates the conditional probability of Y given X.
Choose an option to check your answer.
A.
The support of X minus Y
B.
The confidence of Y → X
C.
The support of X ∪ Y
D.
The number of items in X
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Correct Answer: C. The support of X ∪ Y
Explanation:
A rule is supported by transactions containing both antecedent and consequent.
Choose an option to check your answer.
A.
X = Y for identical sets
B.
X → Y, where X and Y are disjoint itemsets
C.
X + Y as a numeric prediction
D.
X clusters Y using distance
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Correct Answer: B. X → Y, where X and Y are disjoint itemsets
Explanation:
The antecedent X implies an increased occurrence of consequent Y.
Choose an option to check your answer.
A.
An itemset whose support meets or exceeds a minimum threshold
B.
Any itemset with two items
C.
An itemset appearing in every transaction
D.
An itemset with high confidence only
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Correct Answer: A. An itemset whose support meets or exceeds a minimum threshold
Explanation:
Frequency is determined by the user-defined minimum support.
Choose an option to check your answer.
A.
The probability of the consequent only
B.
The number of items divided by features
C.
The average transaction length
D.
The proportion of transactions containing the itemset
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Correct Answer: D. The proportion of transactions containing the itemset
Explanation:
Support normalizes support count by the total number of transactions.
Choose an option to check your answer.
A.
The number of items not in the set
B.
The confidence of every rule from the set
C.
The number of transactions containing the itemset
D.
The number of classes in the dataset
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Correct Answer: C. The number of transactions containing the itemset
Explanation:
Support count is an absolute frequency.
Choose an option to check your answer.
A.
An itemset occurring k times only
B.
An itemset containing exactly k items
C.
A transaction with k customers
D.
A rule with k conclusions
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Correct Answer: B. An itemset containing exactly k items
Explanation:
The value k refers to the number of distinct items in the set.
Choose an option to check your answer.
A.
A collection of one or more items
B.
A sequence of class labels
C.
A set of cluster centroids
D.
A list of numeric predictions
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Correct Answer: A. A collection of one or more items
Explanation:
An itemset represents items considered together in transactional data.
Choose an option to check your answer.
A.
To guarantee every feature is normal
B.
To increase test-set size
C.
To make labels unnecessary
D.
To prevent information from the test set leaking into model development
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Correct Answer: D. To prevent information from the test set leaking into model development
Explanation:
Means, scales, and imputation values estimated from all data expose the model to test information.
Choose an option to check your answer.
A.
Removing duplicate records
B.
Scaling training features
C.
Using information during training that would not be available at prediction time
D.
Using cross-validation
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Correct Answer: C. Using information during training that would not be available at prediction time
Explanation:
Leakage creates unrealistically high evaluation results.
Choose an option to check your answer.
A.
Creating synthetic class labels
B.
Choosing a relevant subset of available attributes
C.
Increasing all feature values
D.
Duplicating informative columns
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Correct Answer: B. Choosing a relevant subset of available attributes
Explanation:
Feature selection reduces dimensionality without transforming the retained variables.
