Microsoft Azure AI Fundamentals (AI-900)Describe fundamental principles of machine learning on AzureMedium
A data scientist is evaluating a machine learning model's performance on a binary classification task. The model predicted 100 positive cases, of which 80 were actually positive. It also predicted 50 negative cases, of which 45 were actually negative. The total number of actual positive cases in the dataset was 90. Which of the following metrics would be most suitable for evaluating the model's ability to correctly identify all actual positive cases?
- AF1-Score
- BAccuracy
- CPrecision
- DRecall
Show answer & explanationAnswer & explanation
Correct answer: D. Recall
Recall (also known as sensitivity) measures the proportion of actual positive cases that were correctly identified by the model. In this scenario, the data scientist wants to evaluate the model's ability to 'correctly identify all actual positive cases', which directly aligns with the definition of recall.
Why the other options are wrong
- A. F1-Score is the harmonic mean of precision and recall, providing a balance between the two.
- B. Accuracy measures the overall proportion of correct predictions (both true positives and true negatives) out of the total number of cases.
- C. Precision measures the proportion of predicted positive cases that were actually correct.
Recall (Sensitivity)
Recall measures the proportion of actual positive cases that a machine learning model correctly identified. It indicates the model's ability to find all relevant instances.
- Calculated as True Positives / (True Positives + False Negatives).
- High recall means fewer false negatives (missed actual positives).
- Important when the cost of missing a positive case is high (e.g., disease detection).
Memory trick: Remember, 'Recall' is how many actual positives you 'recalled' from the dataset.