A data scientist is building a binary classification model to predict fraudulent credit card transactions. The dataset is highly imbalanced: only 1% of transactions are fraudulent. The cost of a false negative is very high because missing a fraudulent transaction can lead to significant financial loss. Which evaluation metric should the data scientist prioritize to minimize false negatives?

Accuracy

Accuracy can be misleading in imbalanced datasets because a model that always predicts the majority class can achieve high accuracy while having poor recall for the minority class.

Precision

Precision measures how many of the predicted positive cases are actually positive. It focuses on false positives, not false negatives, so it does not directly address the goal of minimizing false negatives.

F1 Score

F1 Score is the harmonic mean of precision and recall. While it is useful when both false positives and false negatives are important, it is not as directly aligned with minimizing false negatives as recall alone.

What does this AI-900 question test?

Read the scenario before looking for a memorised answer.

What is the correct answer to this question?

The correct answer is: Recall — Recall (sensitivity) measures the proportion of actual positives correctly identified. To minimize false negatives, maximizing recall is crucial. Accuracy can be misleading with imbalanced data. Precision measures the proportion of predicted positives that are actual positives, which focuses on false positives, not false negatives. F1 Score balances precision and recall, but when the primary goal is to minimize false negatives, recall is the direct and most appropriate metric.

What should I do if I get this AI-900 question wrong?

Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.