A data scientist at an aerospace firm has developed a binary classification model to predict catastrophic engine failures. The positive class represents a "failure" event, which is extremely rare in the operational data. The primary business objective is to avoid missing any potential failures, as a single missed event (a False Negative) is unacceptable due to safety implications. The cost of a False Positive (flagging a healthy engine for inspection) is considered minimal. Which classifier performance metric should be prioritized to best evaluate and optimize the model for this specific requirement?
The correct answer is Recall. Recall, also known as sensitivity or the true positive rate, is calculated as TP / (TP + FN), where TP is True Positives and FN is False Negatives. In scenarios where the cost of a False Negative is very high, such as failing to predict a critical equipment failure, maximizing recall is the primary objective. This metric directly measures the model's ability to identify all actual positive instances.
Precision, calculated as TP / (TP + FP), measures the accuracy of positive predictions. Prioritizing precision would aim to reduce False Positives, which is not the main concern in this scenario.
Accuracy is not suitable for highly imbalanced datasets because a model can achieve a high accuracy score by simply predicting the majority class, while completely failing to identify the rare, critical events.
The F1 score is the harmonic mean of precision and recall and seeks a balance between them. While useful, it does not specifically prioritize minimizing False Negatives, which is the explicit goal here.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
Why is Recall more important than Precision in this scenario?
Open an interactive chat with Bash
What is the impact of an imbalanced dataset on Accuracy?
Open an interactive chat with Bash
How does the F1 Score compare to Recall in this case?