Evaluation Metrics - Problem Set for Competitive Exams

Evaluation Metrics - Problem Set

Q. In which scenario is the F1 Score particularly useful?

A. When false positives are more critical than false negatives
B. When false negatives are more critical than false positives
C. When the class distribution is balanced
D. When the class distribution is imbalanced

Solution

F1 Score is useful in imbalanced class distributions as it balances precision and recall.

Correct Answer: D — When the class distribution is imbalanced

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Q. In which scenario would you prioritize recall over precision?

A. When false positives are more costly than false negatives
B. When false negatives are more costly than false positives
C. When the dataset is balanced
D. When you need a high overall accuracy

Solution

In scenarios where missing a positive case (false negative) is more critical, such as in medical diagnoses, recall should be prioritized over precision.

Correct Answer: B — When false negatives are more costly than false positives

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Q. What does the area under the ROC curve (AUC) represent?

A. The probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance
B. The overall accuracy of the model
C. The precision of the model
D. The recall of the model

Solution

AUC quantifies the model's ability to distinguish between positive and negative classes.

Correct Answer: A — The probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance

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Q. What is the main drawback of using accuracy as an evaluation metric?

A. It does not account for class imbalance
B. It is difficult to calculate
C. It only applies to binary classification
D. It does not provide insights into model performance

Solution

Accuracy can be misleading in imbalanced datasets, as it may not reflect the true performance of the model.

Correct Answer: A — It does not account for class imbalance

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Q. What is the main limitation of using accuracy as a metric?

A. It does not account for class imbalance
B. It is difficult to calculate
C. It only applies to binary classification
D. It requires a large dataset

Solution

Accuracy can be misleading in imbalanced datasets, as it may give a false sense of model performance by not reflecting the true predictive power for minority classes.

Correct Answer: A — It does not account for class imbalance

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Q. What is the main limitation of using accuracy as an evaluation metric?

A. It does not account for false positives and false negatives
B. It is only applicable to regression problems
C. It requires a large dataset to be effective
D. It is difficult to calculate

Solution

Accuracy can be misleading in cases of class imbalance, as it does not differentiate between types of errors.

Correct Answer: A — It does not account for false positives and false negatives

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Q. Which evaluation metric is most appropriate for a model predicting rare events?

A. Accuracy
B. Recall
C. F1 Score
D. Mean Squared Error

Solution

Recall is crucial for rare event prediction as it focuses on capturing as many positive instances as possible.

Correct Answer: B — Recall

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Q. Which metric is best for imbalanced datasets?

A. Accuracy
B. F1 Score
C. Precision
D. Recall

Solution

F1 Score is the harmonic mean of precision and recall, making it a better metric for imbalanced datasets where one class is more significant than the other.

Correct Answer: B — F1 Score

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Q. Which metric would you use to evaluate a model that predicts whether an email is spam or not?

A. Mean Squared Error
B. Accuracy
C. F1 Score
D. R-squared

Solution

F1 Score is preferred for spam detection as it balances precision and recall, especially in imbalanced datasets.

Correct Answer: C — F1 Score

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Evaluation Metrics - Problem Set

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