AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist is training a model to predict whether a patient has a rare disease (1% prevalence). The model predicts 'no disease' for all patients and achieves 99% accuracy, but fails to identify any actual cases. Which metric would best reveal this failure?
⚠ Common exam trap
The trap here is that candidates see 99% accuracy and assume the model is performing well, failing to recognize that accuracy is a poor metric for imbalanced datasets and that recall specifically measures the model's ability to catch rare positive cases.
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
✓
Recall
Recall (sensitivity) measures the proportion of actual positive cases correctly identified. With 1% disease prevalence and a model that predicts 'no disease' for all patients, recall is 0% because zero true positives are found. Accuracy (99%) is misleading here because the model fails to detect any rare disease cases, and recall directly exposes this failure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Precision
Why it's wrong here
Precision is the ratio of true positive predictions to all positive predictions (TP/(TP+FP)). When the model predicts no positives, both TP and FP are zero, so precision is undefined (0/0) and cannot be calculated. Even if one arbitrarily defines precision as 0, it only reflects that no positive prediction was made, not that actual disease cases were missed, so it fails to reveal the model's recall failure.
- ✓
Recall
Why this is correct
Recall (sensitivity) measures the proportion of actual positive cases correctly identified, calculated as TP/(TP+FN). With zero positive predictions, TP = 0 and every actual disease case becomes a false negative, so FN equals the total number of positives, yielding a recall of 0%. This directly exposes that the model misses all patients with the disease, which is precisely the failure mode in question.
- ✗
F1 score
Why it's wrong here
F1 score is the harmonic mean of precision and recall, given by 2·precision·recall/(precision+recall). In this scenario, precision is undefined (0/0) and recall is 0, making F1 either indeterminate or 0 if precision is forced to 0. As a single aggregate, F1 obscures the distinct roles of precision and recall; here the core problem is zero recall, which recall alone communicates clearly and unambiguously.
- ✗
Mean absolute error
Why it's wrong here
Mean absolute error (MAE) computes the average absolute difference between predicted and actual continuous values, typically for regression models. Binary classification outcomes (e.g., disease present/absent) are categorical, not continuous, so MAE is not a meaningful performance metric in this context. For classification, one should use confusion-matrix metrics like recall, precision, or F1, which directly assess prediction correctness for each class.
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