AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A hospital deploys a machine learning model to screen patients for a rare disease. Only 0.1% of patients actually have the disease. The model correctly identifies most positive cases but also flags many healthy patients as potentially having the disease. The hospital wants to minimize the number of healthy patients who are incorrectly told they might have the disease. Which metric should the model optimize?
⚠ Common exam trap
A common mix-up: candidates default to 'Accuracy' for imbalanced datasets or 'Recall' for medical screening, but the question explicitly asks to minimize false positives, which directly points to Precision as the correct metric.
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
✓
Precision
Precision measures the proportion of positive identifications that are actually correct. In this scenario, the hospital wants to minimize false positives (healthy patients incorrectly told they might have the disease). Optimizing precision directly reduces false positives, which is the stated goal.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Recall
Why it's wrong here
Recall (TP/(TP+FN)) measures the fraction of actual positives that are identified, but it completely ignores false positives. A model could achieve perfect recall by flagging every patient, which would generate a large number of false positives and cause exactly the unnecessary anxiety the hospital wants to minimize. Therefore, recall is not the appropriate metric for this objective.
- ✓
Precision
Why this is correct
Precision (TP/(TP+FP)) measures the fraction of positive predictions that are actually correct. Maximizing precision directly reduces false positives, meaning that when the model flags a patient for screening, it is likely a real case, sparing healthy patients from needless follow-up tests and worry. Given the hospital's explicit goal to minimize false positives, precision is the metric that aligns with that clinical priority.
- ✗
F1 score
Why it's wrong here
The F1 score is the harmonic mean of precision and recall, treating false positives and false negatives as equally important. In this scenario, however, the hospital specifically wants to minimize false positives, not balance both error types. A model with a high F1 score might still have low precision and high recall, which would produce many false positives, so F1 does not accurately reflect the stated goal.
- ✗
Accuracy
Why it's wrong here
Accuracy can be misleading in highly imbalanced datasets. A model that predicts all patients as healthy would achieve 99.9% accuracy but would fail to find any positive cases, and it does not directly minimize false positives.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
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