In a confusion matrix, which term represents a correct positive prediction?

• A. True positive
• B. False positive
• C. True negative
• D. False negative

Answer: (A)

In a confusion matrix for binary classification, the main idea is matching predictions to actual outcomes. A correct positive prediction happens when the model says something is positive and it actually is positive. This cell is called a true positive. It represents all the instances the model correctly identified as belonging to the positive class. The other possibilities are false positives (predicted positive but actually negative), true negatives (predicted negative and actually negative), and false negatives (predicted negative but actually positive). For example, in a disease test, a true positive would be someone who really has the disease and is correctly flagged as positive by the test. Understanding true positives is also key to metrics like precision and recall, which depend on how many true positives occur relative to other outcomes.