One common measure derived from a confusion matrix is accuracy, which is calculated as the sum of correct predictions divided by the total number of predictions made. It provides an overall measure of how well the classifier performed. However, accuracy alone may not be sufficient in scenarios with imbalanced class distributions. Precision is another measure that represents the proportion of true positive predictions out of all positive predictions, giving an indication of the classifier's ability to correctly identify positive instances. Recall, also known as sensitivity, is the proportion of true positive predictions out of all actual positive instances and indicates the classifier's ability to capture all positive instances. F1 score, which is the harmonic mean of precision and recall, provides a balanced measure of the classifier's performance, taking both false positives and false negatives into account. These measures can provide valuable insights into the strengths and weaknesses of the classifier and help in making informed decisions about model improvements.