MLA-C01 Practice Question: A machine learning engineer trains a binary…

A machine learning engineer trains a binary classifier and obtains an accuracy of 95% on the test set. The dataset is imbalanced with 95% positive class. What is the most important metric to evaluate the model's performance?

⚠ Common exam trap

The trap here is that candidates see 95% accuracy and assume the model is performing well, failing to recognize that accuracy is inflated by the class imbalance and that the F1 score is the correct metric to evaluate minority class 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

✓

F1 score

With a 95% positive class imbalance, a model that always predicts the majority class achieves 95% accuracy, making accuracy a misleading metric. The F1 score (option B) is the harmonic mean of precision and recall, providing a balanced evaluation of the model's ability to correctly identify the minority class while penalizing false positives and false negatives. This makes it the most important metric for imbalanced binary classification.

Answer analysis

Option-by-option breakdown

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

✗
R-squared
Why it's wrong here
R-squared is a regression metric, not applicable to classification.
✓
F1 score
Why this is correct
F1 score combines precision and recall, making it suitable for imbalanced classification.
✗
Accuracy
Why it's wrong here
Accuracy is not reliable on imbalanced data; a trivial model predicting all positives would get 95%.
✗
RMSE
Why it's wrong here
RMSE is used for regression problems, not classification.

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MLA-C01 Practice Question: A machine learning engineer trains a binary…

Why each option matters

Option-by-option breakdown

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