AAIA · Question #90
Which of the following correctly summarizes the conclusions of the model card excerpt provided? Model Card ?Electrical Grid Predictive Maintenance Model Model Information: Description: AI model design
The correct answer is D. F1 indicates that the model identifies true maintenance needs 76% of the time.. Option D is the best interpretation. The F1 score (76%) is the harmonic mean of Precision (60%) and Recall (95%), representing the model's overall balanced ability to identify true maintenance needs-accounting for both false positives and false negatives. It is the most holistic
Question
Which of the following correctly summarizes the conclusions of the model card excerpt provided? Model Card ?Electrical Grid Predictive Maintenance Model Model Information:
Description: AI model designed to predict maintenance needs for electrical grid components, reduce unplanned downtime, and improve grid reliability. Inputs: Real-time sensor data, historical maintenance records, and operational logs. Outputs: Maintenance needs predictions for 60 & 90 days. Evaluation:
Approach: Cross-validation and validation of accuracy, precision, and recall. Results: Accuracy 72%; Precision 60%; Recall 95%; F1 76%
Options
- AThe AI model correctly predicts maintenance needs 95% of the time.
- BThe electrical grid uptime is expected to be 72% of the time.
- CGrid failure is predicted to occur after 90 days.
- DF1 indicates that the model identifies true maintenance needs 76% of the time.
How the community answered
(50 responses)- A4% (2)
- B4% (2)
- C8% (4)
- D84% (42)
Explanation
Option D is the best interpretation. The F1 score (76%) is the harmonic mean of Precision (60%) and Recall (95%), representing the model's overall balanced ability to identify true maintenance needs-accounting for both false positives and false negatives. It is the most holistic single metric of the model's classification performance. Option A is wrong because 95% is the Recall metric, which only measures the proportion of actual maintenance needs the model successfully detects (true positives out of all real positives)-not overall correctness. Option B is wrong because Accuracy (72%) measures model prediction performance, not physical grid uptime. Option C is wrong because the 90-day figure refers to the prediction horizon (how far ahead the model forecasts), not when a failure is predicted to occur.
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