Question: What is the purpose of the term 'confusion matrix' in classification?

Answer: A confusion matrix is a table that evaluates the performance of a classification model by presenting the counts of true positives, true negatives, false positives, and false negatives. It is useful for assessing model accuracy, precision, recall, and F1 score.

Example:

For a binary classification problem, a confusion matrix might look like: [[TN, FP], [FN, TP]].