Accuracy

Accuracy is an evaluation metric for Classification models: the proportion of predictions the model got right.

$\text{Accuracy} = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{TN} + \text{FP} + \text{FN}}$

It's easy to understand, but it can be misleading on imbalanced datasets. If 99% of examples are negative, a model that always predicts negative gets 99% accuracy while being useless. In those cases, metrics like Precision, Recall and the F1-Score, or looking at the full Confusion Matrix, tell you more.