What is a confusion matrix and how is it used to evaluate model performance?

A confusion matrix is a table used to evaluate the performance of a classification model by comparing actual vs. predicted classifications. It shows the number of true positives, true negatives, false positives, and false negatives, and can be used to calculate various evaluation metrics like precision, recall, and accuracy.