AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist trains a binary classification model to detect fraudulent credit card transactions. The dataset contains 99.5% legitimate transactions and 0.5% fraudulent transactions. The model predicts every transaction as legitimate and achieves 99.5% accuracy on the test set. Which metric would best reveal that the model is failing to identify any fraudulent transactions?
⚠ Common exam trap
The trap here is that candidates see 99.5% 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 find positive cases (fraud).
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 (also known as sensitivity) measures the proportion of actual positive cases correctly identified by the model. In this scenario, the model predicts all transactions as legitimate, so it correctly identifies zero fraudulent transactions, giving a recall of 0%. Accuracy alone is misleading because the dataset is highly imbalanced (99.5% legitimate, 0.5% fraudulent), and a 99.5% accuracy can be achieved by simply predicting the majority class. Recall directly reveals the model's failure to detect any fraud.
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 calculated as true positives divided by all predicted positives (true positives + false positives). Since the model predicts zero fraudulent transactions, there are no predicted positives at all, so precision becomes 0/0, which is undefined. While this indicates the model is not making positive predictions, it does not directly quantify the model's failure to identify actual fraud, making it uninformative for evaluating detection capability.
- ✓
Recall
Why this is correct
Recall, or sensitivity, is the ratio of true positives to all actual positives (true positives + false negatives). With the model predicting no fraud, true positives are 0 while false negatives equal the total number of actual fraudulent transactions, yielding a recall of 0. This directly measures the model's inability to catch any positive cases, which is exactly the failure mode described in the scenario, so recall is the most appropriate metric to highlight the problem.
- ✗
F1 score
Why it's wrong here
The F1 score is the harmonic mean of precision and recall. With recall at 0, the F1 score would also be 0, but recall is the more direct indicator because it specifically measures the model's ability to find positive cases.
- ✗
Mean Absolute Error (MAE)
Why it's wrong here
Mean Absolute Error (MAE) is designed for regression tasks, measuring the average absolute difference between continuous predicted values and true values. Binary classification outputs discrete class labels or probabilities, not continuous quantities, so applying MAE to assess fraud detection is conceptually invalid. Even if probabilities were used, MAE would not capture the recall or precision trade-offs that matter for classification, rendering it inapplicable for evaluating this model's failure to detect fraud.
Go deeper
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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