Other than accuracy rate, there are various metrics for machine learning to measure how “accurate” the model is.
Some popular ones for binary classification are sensitivity (true positive rate) and specificity (true negative rate).
In computer science, recall and precision are also common metrics.
It can be quite confusing to remember offhand what each metric means, and how they are related.
To summarize, the following are equivalent (for binary classification):
sensitivity = recall of positive class
specificity = recall of negative class
Other than the above metrics mentioned, there are also many other metrics, such as F1 score, etc.