AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist trains a binary classification model to detect a rare disease. The dataset contains 99% negative cases and only 1% positive cases. The model predicts all cases as negative, achieving an accuracy of 99% on the test set. However, the business requires the model to identify as many positive cases as possible. Which metric should the data scientist examine to best reveal that the model is failing to identify any positive cases?
⚠ Common exam trap
Watch out — candidates often choose accuracy as the primary metric, overlooking that high accuracy can mask poor performance on the minority class in imbalanced datasets.
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 by the model. With all predictions as negative, recall is 0%, directly revealing the model's failure to detect any positive cases despite the high accuracy.
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 measures how trustworthy a positive prediction is: it is the fraction of predicted positives that are actually positive (TP / (TP + FP)). Because the model never predicts the positive class, both TP and FP are 0, so precision is mathematically undefined as 0/0 rather than a useful numeric score. If you force a value like 0%, it would misleadingly suggest the model is doing poorly on its positive predictions, when in fact it makes no positive predictions at all. Thus precision cannot expose the fundamental problem that every true positive is being missed.
- ✓
Recall
Why this is correct
Recall, also called sensitivity or true positive rate, computes the proportion of actual positive cases the model correctly identifies, TP / (TP + FN). With no positive predictions, TP = 0 while FN equals the total number of real positive examples, so recall is exactly 0%. This zero is directly meaningful: it tells you the model failed to catch every single positive case, which is the core failure in this scenario. Unlike precision, recall does not depend on how many false positives are made, so it cleanly isolates the model's inability to detect positives.
- ✗
F1 score
Why it's wrong here
The F1 score is the harmonic mean of precision and recall, F1 = 2 * (P * R) / (P + R), so when recall is 0, F1 becomes 0. However, F1 collapses two distinct error types into one number; a 0 F1 could also result from a model that predicts everything as positive and has very low precision. It therefore does not pinpoint the specific cause — that the model makes zero positive predictions — while recall alone instantly reveals the model is missing all positives. Reporting F1 here would obscure the diagnostic clarity that recall provides.
- ✗
AUC-ROC
Why it's wrong here
AUC-ROC measures the model's ability to distinguish between classes. A model that always predicts negative has an AUC of 0.5, indicating no discriminative ability, but this metric does not directly reveal that no positives are being caught.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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