A confusion matrix summarizes how a classification model’s predictions compare with known labels. In binary classification, it separates true positives, true negatives, false positives, and false negatives. Multiclass matrices extend the same idea across every pair of actual and predicted categories. The table exposes error patterns hidden by a single accuracy score and provides the counts used to calculate precision, recall, specificity, and other metrics. Interpretation should consider class prevalence and the real cost of each mistake. When a decision threshold can change, teams often inspect multiple confusion matrices or threshold curves instead of treating one operating point as permanent.
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