Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case?

1 Accuracy is not a good metric for imbalanced class problems.

2 Accuracy is a good metric for imbalanced class problems.

3 Precision and recall metrics are good for imbalanced class problems.

4 Precision and recall metrics aren’t good for imbalanced class problems.

Question

Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case?

1 Accuracy is not a good metric for imbalanced class problems.

2 Accuracy is a good metric for imbalanced class problems.

3 Precision and recall metrics are good for imbalanced class problems.

4 Precision and recall metrics aren’t good for imbalanced class problems.

BlackTom AI · Accepted Answer

1) Relevant knowledge points and concepts
Accuracy = (TP + TN) / (TP + TN + FP + FN). In imbalanced-class problems (one class far more frequent), accuracy can be dominated by the majority-class correct predictions and therefore be misleading. Precision = TP / (TP + FP) measures correctness among positive predictions. Recall (sensitivity) = TP / (TP + FN) measures how many actual positives are found. Precision and recall (and F1) focus on the minority/positive class and are therefore more informative for imbalanced settings.

2) Step-by-step reasoning
If your dataset is heavily imbalanced, a classifier that always predicts the majority class will achieve high accuracy but fail to identify minority-class instances. Therefore high accuracy alone does not imply good performance on the minority class. Precision and recall directly quantify performance on the positive/minority class and reveal failures that accuracy conceals.

3) Calculations and intermediate steps (concrete example)
Assume 1,000 test samples with 1% positive (10 positives) and 990 negatives. A trivial classifier predicts “negative” for every sample:
- TP = 0 (no positives predicted correctly)
- FN = 10 (all positives missed)
- TN = 990
- FP = 0
Accuracy = (TP + TN) / 1000 = (0 + 990) / 1000 = 0.99 = 99%
Precision = TP / (TP + FP) = 0 / 0 → effectively 0 (no positive predictions)
Recall = TP / (TP + FN) = 0 / 10 = 0
This shows 99% accuracy while precision and recall are zero → accuracy is not a reliable indicator here.

4) Why the correct answer is correct
Statement 1 (“Accuracy is not a good metric for imbalanced class problems”) is true because accuracy can be dominated by the majority class. Statement 3 (“Precision and recall metrics are good for imbalanced class problems”) is true because they directly evaluate how well the minority class is detected and how many positive predictions are correct.

5) Why other options are incorrect
Statement 2 is false: accuracy can be misleading on imbalanced data. Statement 4 is false: precision and recall are in fact appropriate metrics for imbalanced problems.

6) Conclusion
The correct choice is “1 and 3,” which matches the provided answer.

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