One table, four outcomes
The confusion matrix is a simple table that breaks every prediction a classifier made into one of four categories, true positives, false positives, true negatives, and false negatives. True positives are cases correctly predicted as positive, true negatives are cases correctly predicted as negative, false positives are negative cases wrongly predicted as positive, and false negatives are positive cases wrongly predicted as negative. Every single prediction the model ever made lands in exactly one of these four boxes.
This table is not just a diagnostic curiosity, it's the actual source of every metric covered in this course. Accuracy is the correct predictions, true positives plus true negatives, divided by everything. Precision is true positives divided by everything predicted positive, true positives plus false positives. Recall is true positives divided by everything actually positive, true positives plus false negatives. None of these formulas exist separately from the confusion matrix, they are all just different ways of slicing the same four numbers.