What is a Confusion Matrix?

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Looking at the error rate of a machine learning model is not the only way to look at the performance of a model. For classification AI which are AI models that classify data according to data it has learned, a confusion matrix, also known as error matrix, is used to judge the performance of a machine. It is a way of visualizing the performance of a machine learning model.

In this video on What is a Confusion Matrix we show you what a confusion matrix looks like. Essentially, it has four quadrants. Across the top represents predictions of what the data is and across the side represents the actual data- the truth. The point of a confusion matrix is to observe the strengths and weaknesses of a model. Now imagine an AI that identifies pictures of dogs.

Here is a visual of 15 images of dogs this AI model processed. Whatever is circled is the AI predictions of which images are dogs . Therefore, the accuracy of the model is 60%. It correctly identified 9 out of 15 images as dogs For machine learning, however, accuracy is not relied on because it does not tell you for AI models. It is just as important to know what the machine did correctly as what the machine light have missed.

This is where the confusion matrix comes in. In the confusion matrix for the same dog classifier image recognition AI we take note of the total number of data points, which are 15. Then we start on the top left. Here we count how many images the model predicted were not dogs , that in fact were not dogs. Here you count three. These are your true negatives- a correct prediction

Watch the rest of the video to find out the other three data points of how this confusion matrix model works.

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