Accuracy is the ratio of the correctly classified observations. In binary classification settings, it checks the sum of TP and TN over all the predictions:
$$ \mathrm{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} $$It is very important to use this metric on datasets that do not suffer from class imbalance; for example, a model that always predicts “False” on a dataset having 99.9% of False observations will have a very high 0.999 accuracy score but it won’t be meaningful for detecting the True observations.
In case of class imbalance, the appropriate metrics are precision, recall or F1-score depending on the task.