Confusion Matrix
What is Confusion Matrix?
A confusion matrix is a tabular tool used in Edge AI to evaluate the accuracy of machine learning models by comparing predicted outcomes against actual results. Also called an error matrix, it highlights true positives, false positives, true negatives, and false negatives, making it easier to understand model performance at the edge.
A confusion matrix is also a performance measurement tool for machine learning models, especially in Edge AI applications. It summarizes prediction results in a table to help developers visualize where models are making correct or incorrect predictions in real-time edge devices.
Why Is It Used?
It is used to identify and quantify errors, optimize model accuracy, and ensure reliable decision-making in Edge AI systems, where processing occurs locally on devices like sensors, cameras, or industrial machines.
How Is It Used?
Developers generate a confusion matrix after running a model on labeled test data. Each cell shows the count of predictions versus actual labels, allowing teams to refine models for better precision, recall, and overall performance on edge devices.
Types of Confusion Matrix
Binary Confusion Matrix: Compares two classes (e.g., yes/no, defect/no defect).
Multiclass Confusion Matrix: Handles multiple categories (e.g., multiple object types in video surveillance).
Normalized Confusion Matrix: Shows proportions instead of raw counts for clearer percentage-based insights.
Benefits of Confusion Matrix
Quickly identifies model weaknesses in edge deployments.
Improves precision and recall for real-time applications.
Enables actionable insights for continuous learning at the edge.
Enhances reliability of Edge AI solutions in industries like manufacturing, healthcare, and retail.