Cybersecurity: When we talk about the confusion matrix

Confusion Matrix The Confusion Matrix is a table that summarizes the number of true and false predictions made by a classifier. It is used to measure the performance of a classification model. It can be used to assess the performance of a classification model by calculating performance indicators such as accuracy, precision, recall, and F1 score. If you are working with an unbalanced dataset, you had better use the confusion matrix as the endpoint for your machine learning model. Here are the basic terms that will help us identify the metrics we are looking for.