For binary classification it separates true positives, false positives, true negatives, and false negatives. Multiclass matrices reveal which labels are confused and supply the counts used by precision, recall, and related metrics.
A confusion matrix counts predicted classes against actual classes to show where a classifier makes errors.
For binary classification it separates true positives, false positives, true negatives, and false negatives. Multiclass matrices reveal which labels are confused and supply the counts used by precision, recall, and related metrics.