A confusion matrix is a table used in the context of supervised learning in artificial intelligence and machine learning to evaluate the performance of a classification model. This matrix compares the actual labels of the test data with the labels predicted by the model and shows how many data were correctly classified and how many were misclassified.

The confusion matrix generally has four entries, which are: true positives (TP), false positives (FP), true negatives (TN) and false negatives (FN). True positives represent cases where the model correctly predicted the presence of a specific class, false positives represent cases where the model incorrectly predicted the presence of a specific class, true negatives represent cases where the model correctly predicted the absence of a specific class, and false negatives represent cases where the model incorrectly predicted the absence of a specific class.

The confusion matrix is a useful tool for evaluating the performance of a classification model and adjusting its parameters to improve its accuracy. In addition, several evaluation metrics, such as accuracy, recall, F1-score and error rate, can be calculated from the information provided by the confusion matrix.