MLS-C01 Modeling Practice Question

A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). The model achieves 95% accuracy but only 10% recall on the positive class. Which metric should be used to evaluate model performance?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

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F1 score

With imbalanced data (95% negative, 5% positive), accuracy is high despite poor positive class performance. The F1 score (harmonic mean of precision and recall) is a better metric because it captures both false positives and false negatives. Here, recall is only 10%, so even if precision is high, F1 score will be low, reflecting poor model quality.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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F1 score
Why this is correct
F1 score is the harmonic mean of precision and recall. It is appropriate for imbalanced datasets because it balances both metrics.
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Accuracy
Why it's wrong here
Accuracy is misleading on imbalanced data because high accuracy can be achieved by simply predicting the majority class.
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Recall
Why it's wrong here
Recall alone ignores precision. With imbalanced data, recall may be high if the model predicts most positives correctly, but precision could be low.
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Precision
Why it's wrong here
Precision alone ignores recall. A model could achieve high precision by only predicting positive when highly confident, but miss many positives.

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MLS-C01 Modeling Practice Question

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