Recall
What is Recall?
Recall in Edge AI refers to the model’s ability to correctly identify all relevant positive instances from data processed at the edge. In simpler terms, it measures how many actual positive cases the Edge AI system successfully detects. It’s often used alongside precision to evaluate overall model performance in real-time environments.
Why Is It Used?
Recall is used to ensure that an Edge AI model doesn’t miss critical detections—especially in scenarios like predictive maintenance, surveillance, or medical diagnostics. High recall means fewer false negatives, which is crucial when missing an event (e.g., equipment failure or security breach) can have significant consequences.
How Is It Used?
In Edge AI systems, recall is calculated by comparing model predictions against ground truth data collected at the edge. It’s expressed as:
Recall = True Positives / (True Positives + False Negatives)
Edge engineers and data scientists use recall metrics to fine-tune models deployed on edge devices, ensuring reliable decision-making even under low-latency or bandwidth-constrained conditions.
Types of Recall
Macro Recall: Average recall across all classes.
Micro Recall: Aggregated recall across all instances.
Weighted Recall: Adjusted recall considering class imbalance.
Benefits of Recall
Improved Reliability: Reduces the risk of missing critical detections.
Enhanced Model Optimization: Supports better balance with precision.
Real-Time Responsiveness: Ensures Edge AI systems can act on every relevant signal.
Operational Safety: Ideal for mission-critical applications where accuracy drives outcomes.