Understanding the Confusion Matrix

The previous chapter discussed the Decision Matrix and Pugh Matrix to help us make informed decisions. The next step beyond Decision Matrix and Pugh Matrix is the Confusion Matrix, a most aptly named statistical construct. A Confusion Matrix is used to evaluate the performance of an AI / ML model by comparing the predicted results with the actual results. The matrix shows the number of true positive, true negative, false positive, and false negative predictions made by the model. In other words, it shows the number of correct and incorrect predictions made by the model. It can be used to calculate a variety of AI / ML model performance metrics, such as precision, recall, and accuracy, which can help to identify the strengths and weaknesses of the AI / ML model.For example, let’s say I’m a shepherd, and my job is to correctly distinguish between my sheepdog protecting my sheep and a wolf out to eat my sheep. Unfortunately, I have bad eyesight...