Whenever you're ready, walk me through how you'd approach this.
You are training a supervised model to predict rare vehicle component failures from historical service and telemetry data. Positive examples are scarce, but missing a true failure is costly and too many false alerts will overwhelm operations.
How do you handle highly imbalanced datasets when training a model to predict rare vehicle component failures?