When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should…

In a predictive maintenance scenario like forecasting wind turbine failure, the cost of a False Negative (failing to predict an actual failure) is significantly higher than the cost of a False Positive (predicting a failure that does not occur). A missed failure could lead to catastrophic damage, extended downtime, and safety hazards. Recall, also known as Sensitivity or the True Positive Rate, measures the model's ability to identify all actual positive cases (TP / (TP + FN)). Therefore, maximizing recall is the primary objective to ensure as few actual failures as possible are missed, even if it means accepting more false alarms.

A. Precision: This metric (TP / (TP + FP)) focuses on minimizing false alarms. While important, it is secondary to the critical need to not miss an actual failure.

B. Specificity: This metric (TN / (TN + FP)) measures the model's ability to correctly identify non-failing turbines, which does not address the primary goal of catching failures.

C. Accuracy: This is often a misleading metric for imbalanced datasets, which are common in fault detection. A model could achieve high accuracy by simply always predicting the majority class (no failure).

1. Powers

D. M. W. (2011). Evaluation: From Precision

Recall and F-Measure to ROC

Informedness

Markedness & Correlation. Journal of Machine Learning Technologies

2(1)

37-63. (This paper provides a foundational overview of evaluation metrics

discussing the contexts where recall is prioritized over precision

such as in diagnostic tasks where missing a positive case is critical. See Section 2

"Precision and Recall").

2. Provost

F.

& Fawcett

T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly Media. (Chapter 7

"Evaluating Models

" explains that in problems with imbalanced classes and high costs for false negatives

such as fraud detection or medical diagnosis

recall is a more critical metric than accuracy or precision).

3. Stanford University. (n.d.). CS229 - Machine Learning: Lecture Notes - Evaluation Metrics. (In discussions on skewed classes

these materials emphasize that when the cost of a false negative is high

such as in cancer detection

evaluating a model based on recall and precision is more informative than using accuracy. The same logic applies directly to critical equipment failure prediction).