What is a confusion matrix?

a confusion matrix is a metric that is often used to measure the performance of a classification algorithm. let us consider a spam classifier, our classifier needs to determine whether the mail is spam or not. The predicted classes are represented in the columns of the matrix, whereas the actual classes are in the rows of the matrix. We then have four cases. True positives (TP): the cases for which the classifier predicted ‘spam’ and the emails were actually spam.
True negatives (TN): the cases for which the classifier predicted ‘not spam’ and the emails were actually real.
False positives (FP): the cases for which the classifier predicted ‘spam’ but the emails were actually real.
False negatives (FN): the cases for which the classifier predicted ‘not spam’ but the emails were actually spam.