easyMultiple Choice
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 quantifies variance explained in regression, requiring continuous targets; it cannot be computed from binary class labels and ignores the class imbalance entirely. It is tempting because R-squared is a familiar goodness-of-fit measure, and would be correct when evaluating a regression model predicting a continuous outcome.
- ✓
F1 score
Why this is correct
With 95% positives, a model predicting only the majority class scores 95% accuracy yet detects no negatives. F1 score balances precision and recall on the minority class, exposing that failure, whereas accuracy and raw recall remain misleadingly high.
- ✗
Accuracy
Why it's wrong here
Accuracy reports the proportion of correct predictions, so a model predicting the majority class every time already scores 95% on this dataset, masking failure to detect the minority class. It is tempting because accuracy is intuitive and adequate when classes are balanced, which is not the case here.
- ✗
RMSE
Why it's wrong here
RMSE measures the magnitude of error for continuous predictions, so it cannot be computed from class labels and says nothing about minority-class detection. It is tempting because RMSE is a standard regression metric, and would be the right choice when predicting a continuous target such as price rather than a binary class.
Go deeper
Related to this question
About these practice questions
This MLA-C01 question is part of Courseiva's 665-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.