Classification accuracy and error rate are primary metrics used to evaluate the performance of a classification model. Let TP, FP, TN, and FN denote the number of true positives, false positives, true negatives, and false negatives, respectively.

The classification Accuracy is the proportion of true results (both true positives and true negatives) among the total number of cases examined: $\text{Accuracy} = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{TN} + \text{FP} + \text{FN}}$

The Error Rate is the proportion of misclassified cases (both false positives and false negatives) among the total number of cases examined, which is also equal to 1 minus Accuracy: $\text{Error Rate} = \frac{\text{FP} + \text{FN}}{\text{TP} + \text{TN} + \text{FP} + \text{FN}} = 1 - \text{Accuracy}$