AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
What is recall (sensitivity) in the context of binary classification model evaluation?
⚠ Common exam trap
Test-takers frequently confuse recall with precision (Option A) because both involve true positives, but recall focuses on actual positives while precision focuses on predicted positives.
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
✓
The proportion of actual positives that the model correctly identified
Recall (sensitivity) measures the proportion of actual positive cases that the model correctly identifies. In binary classification, it answers: 'Of all the truly positive instances, how many did the model catch?' This is critical in scenarios where missing a positive (false negative) is costly, such as disease screening or fraud detection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The proportion of positive predictions that are actually correct
Why it's wrong here
Precision, also called positive predictive value, is the proportion of predicted positive cases that are actually positive, computed as TP / (TP + FP). Recall instead uses the actual positive cases as the denominator, so it tells you how many of the real positives were captured. Precision and recall are complementary, but this option precisely defines precision, not recall.
- ✓
The proportion of actual positives that the model correctly identified
Why this is correct
Recall measures the fraction of actual positive instances that the model correctly identifies, mathematically TP / (TP + FN). It is also called sensitivity or true positive rate, and it directly reflects how well the model avoids missing positive cases. This makes it the correct definition for this question.
- ✗
The overall proportion of predictions that match the actual labels
Why it's wrong here
Accuracy is the ratio of all correct predictions (both true positives and true negatives) to the total number of cases, (TP + TN) / N. Recall ignores true negatives entirely and focuses solely on the positive class detection rate. A model that predicts only the majority class can achieve high accuracy while still having very low recall for the minority class, so this option describes accuracy, not recall.
- ✗
How quickly the model can be updated with new training data
Why it's wrong here
The speed at which a model can be retrained or updated with new data is an operational property of the training pipeline, such as online learning or batch retraining. Recall is a static evaluation metric computed from the model's predictions on labeled test data, regardless of how quickly the model was built. This option confuses model maintenance logistics with classification performance measurement.
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Related to this question
Learn chapter
Regression and Classification
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
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.
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