MCQ Collection
Data Mining MCQs
Practice Data Mining questions with answers and explanations.
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Correct Answer: C. It explores itemsets level by level according to their size
Explanation:
Apriori completes frequent one-itemsets before two-itemsets, then three-itemsets, and so on.
This level-wise strategy enables subset-based pruning.
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Correct Answer: C. When transactions share few prefixes
Explanation:
Limited prefix overlap reduces path sharing.
The tree may then approach the size of the filtered transaction data.
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Correct Answer: C. The ability to perform well on unseen observations
Explanation:
Generalization is the central goal of predictive modeling.
It is evaluated using data not used to fit the model.
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Correct Answer: C. A measure of class impurity or uncertainty
Explanation:
Entropy is low when a node contains mostly one class.
It is highest when classes are evenly mixed.
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Correct Answer: C. The longest root-to-leaf path
Explanation:
Limiting depth restricts model complexity.
A shallow tree may generalize better but can underfit.
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Correct Answer: C. Their evidence may be counted multiple times
Explanation:
The independence model treats correlated copies as separate evidence.
This can create overconfident and distorted posterior probabilities.
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Correct Answer: D. More itemsets may qualify as frequent and the search space can expand sharply
Explanation:
A lower threshold admits patterns with smaller counts.
This often produces many more candidates and frequent itemsets.
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Correct Answer: D. All combinations of items along the path can form patterns with the suffix
Explanation:
A single path represents nested item occurrence.
Combinations can be generated directly using minimum node counts.
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Correct Answer: D. A table comparing actual and predicted class labels
Explanation:
The matrix counts true positives, false positives, true negatives, and false negatives.
It supports multiple classification metrics.
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Correct Answer: D. The reduction in entropy produced by a split
Explanation:
Information gain compares parent impurity with weighted child impurity.
A larger reduction indicates a more informative split.
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Correct Answer: D. Trees are high-variance learners whose early split choices affect later structure
Explanation:
A modest change can alter the best root split and cascade through the tree.
Ensemble methods reduce this instability.
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Correct Answer: D. A mean and a variance
Explanation:
The normal likelihood is determined by class-specific means and variances.
These statistics are estimated from training observations.
