Accuracy is a commonly used metric to measure the performance of a classification model. It is calculated by dividing the number of correctly classified instances by the total number of instances in the testing data. A higher accuracy value indicates a more accurate classifier.Mathematically, accuracy can be expressed as:Accuracy = (Number of correctly classified instances / Total number of instances) * 100However, accuracy alone may not be sufficient to evaluate the performance of a classifier, especially when the dataset is imbalanced or when the misclassification of certain classes is more critical than others. In such cases, other metrics such as precision, recall, and F1 score are also considered.