When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus?

Precision measures the proportion of predicted failures that are actual failures; optimizing precision reduces false alarms but risks missing real failures, which is the more dangerous outcome in this scenario.

Specificity measures the true negative rate - correctly identifying non-failure cases - which is not the primary concern when the goal is to catch all actual failures.

Accuracy can be misleading for rare-event prediction like equipment failures because a model that always predicts 'no failure' may achieve high accuracy while completely failing to identify any real failures.

Recall measures the proportion of actual positive cases (real failures) that the model correctly identifies, minimizing false negatives. In a failure prediction context, a false negative means a failure is not predicted and no preventive action is taken, potentially resulting in catastrophic equipment damage, safety hazards, and costly unplanned downtime. Because the consequence of missing a real failure is far more severe than acting on a false alarm, maximizing recall is the primary objective for this type of predictive maintenance model.