Practice Library
All MCQs
Browse exam-wise, subject-wise, and country-wise MCQs with explanations.
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Correct Answer: A. The harmonic mean of precision and recall
Explanation:
F1 balances precision and recall in one measure.
The harmonic mean is low when either component is low.
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Correct Answer: D. The proportion of actual negatives correctly identified
Explanation:
Specificity is the true negative rate.
It measures how well the classifier avoids false alarms among negatives.
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Correct Answer: C. The proportion of actual positives correctly identified
Explanation:
Recall is also called sensitivity or true positive rate.
It is important when missing a positive case is costly.
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Correct Answer: B. The proportion of predicted positives that are truly positive
Explanation:
Precision measures the reliability of positive predictions.
It is important when false positives are costly.
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Correct Answer: A. The proportion of all predictions that are correct
Explanation:
Accuracy equals correct predictions divided by total predictions.
It can be misleading when classes are highly imbalanced.
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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: 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: B. Using a model too simple to capture important patterns
Explanation:
An underfit model has high bias and performs poorly even on training data.
More appropriate features or model complexity may be needed.
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Correct Answer: A. Learning training-specific noise that does not generalize
Explanation:
An overfit model performs well on training data but poorly on new data.
Excessive complexity and leakage are common causes.
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Correct Answer: D. Choosing models and hyperparameters before final testing
Explanation:
Validation data guide development decisions without touching the test set.
The final evaluation is then performed once on held-out test data.
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Correct Answer: C. Estimating performance on unseen data after model selection
Explanation:
The test set simulates future cases not used in development.
Using it during tuning makes the estimate optimistically biased.
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Correct Answer: B. Fitting the model's parameters or decision rules
Explanation:
The model learns patterns from training examples.
Performance must be assessed on separate data to estimate generalization.
