Recall (also sensitivity or true positive rate) is the ratio of True Positives ($TP$) to the sum of true positives and false negatives ($TP + FN$). $\text{recall} = \frac{TP}{TP + FN}$. A model with a perfect recall evaluation means that the model is predicting all negative examples correctly (i.e. no false negatives).