What is a confusion matrix?

A confusion matrix is a table used to evaluate the performance of a classification model. It has four entries: True Positives (TP) — correctly predicted positive, True Negatives (TN) — correctly predicted negative, False Positives (FP) — predicted positive but actually negative (Type I error), and False Negatives (FN) — predicted negative but actually positive (Type II error). From these, we compute metrics like Accuracy, Precision, Recall, F1-Score, and Specificity.