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Browse exam-wise, subject-wise, and country-wise MCQs with explanations.
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Correct Answer: A. An SVM that allows some margin violations or misclassifications
Explanation:
Slack variables permit imperfect separation.
The model balances a wide margin against training errors.
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Correct Answer: D. An SVM that permits no training classification violations
Explanation:
Hard-margin separation requires perfectly linearly separable training data.
It is highly sensitive to noise and outliers.
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Correct Answer: C. The distance between the separating hyperplane and the nearest class points
Explanation:
The margin measures separation around the decision boundary.
SVM optimization attempts to maximize it while controlling violations.
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Correct Answer: B. Training points that determine the position of the decision boundary
Explanation:
Support vectors lie on or inside the margin boundaries.
Moving non-support-vector points slightly often does not change the classifier.
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Correct Answer: A. Find a separating hyperplane with the maximum margin
Explanation:
SVM seeks a boundary that leaves the widest gap between classes.
A larger margin is associated with better robustness.
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Correct Answer: D. Numeric code differences may imply nonexistent order and distances
Explanation:
Encoding red, blue, and green as 1, 2, and 3 creates artificial spacing.
Appropriate one-hot encoding or mixed-type distances should be used.
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Correct Answer: C. To accelerate retrieval of nearby points
Explanation:
Structures such as k-d trees partition feature space for faster queries.
Their effectiveness can decline in very high dimensions.
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Correct Answer: B. They distort distance without adding predictive information
Explanation:
Distance treats included dimensions as part of similarity.
Feature selection or metric learning can improve neighborhoods.
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Correct Answer: A. Predicting a numeric value from the responses of nearby training points
Explanation:
The prediction is often the mean or weighted mean of neighbor targets.
It is a local nonparametric regression method.
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Correct Answer: D. Use distance-weighted voting or a deterministic tie rule
Explanation:
Weighted votes prefer classes represented by closer neighbors.
A predefined deterministic rule ensures reproducibility.
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Correct Answer: C. Majority-class points are more likely to dominate local votes
Explanation:
A dense majority class can surround minority observations.
Resampling, class weights, or local threshold adjustments may help.
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Correct Answer: B. It may need to compare a query with many training observations
Explanation:
A brute-force search scans the stored training set for each query.
Indexes or approximate neighbor methods can improve speed.
