Everyone’s talking about it. But does everyone understand?

In machine vision, we often talk about AI, defect detection or the accuracy of visual analysis. But how do you know if an AI model really works? That’s where the confusion matrix comes in.

An essential tool for evaluating an algorithm’s performance, it enables AI predictions to be compared with reality. It classifies results into true positives, false positives, false negatives and true negatives. A correct reading of this matrix changes everything, as it can reveal biases, validate the reliability of a model or guide its improvement.

As far as we’re concerned, we use the confusion matrix from the very first phases of a project. It enables us to fine-tune our algorithms for greater accuracy, while at the same time guaranteeing a level of quality control adapted to industrial requirements.

Do you have any questions? Contact Baptiste Amato-Gagnon directly via Linkedin.