F1 Score
What is F1 Score?
The F1 Score, also called the F-measure, is a key performance metric in Edge AI that balances precision and recall to evaluate model accuracy, especially for tasks with uneven class distributions. It shows how well an AI system identifies relevant data while minimizing false positives and negatives.
Why Is It Used?
F1 Score is used to measure the effectiveness of Edge AI models in real-world scenarios where both false alarms and missed detections carry significant consequences. It ensures reliability in critical applications like predictive maintenance, smart surveillance, and IoT analytics.
How Is It Used?
Edge AI engineers calculate the F1 Score using precision (correct positive predictions / total predicted positives) and recall (correct positive predictions / total actual positives). It guides model optimization and helps select the best-performing algorithm for deployment on resource-constrained edge devices.
Types of F1 Score
Macro F1 Score: Averages F1 across all classes equally, ideal for multi-class problems.
Micro F1 Score: Aggregates contributions of all classes, useful when class sizes vary.
Weighted F1 Score: Considers class frequency to provide a more representative performance measure.
Benefits of F1 Score
Provides a single metric combining precision and recall.
Ideal for imbalanced datasets often seen in Edge AI deployments.
Helps optimize AI models for faster, accurate decision-making on edge devices.