The "recall" metric is a measure of the fraction of positive outcomes that were correctly scored. The higher the number the better the model is at identifying who is likely to generate a success.
Recall is therefore defined and computed as:
The terms true positives (tp), true negatives (tn), false positives (fp), and false negatives (fn) (see wikipedia: Type I and type II errors for definitions) compare the results of the classifier under test with trusted external judgments. The terms positive and negative refer to the classifier's prediction (sometimes known as the expectation), and the terms true and false refer to whether that prediction corresponds to the external judgment (sometimes known as the observation).
Consider a small example in terms of scoring leads who will convert and who won't. Let's say we are looking at 10 leads, 5 of which will convert (the positive outcomes) and 5 of which won't. The model predicts that 3 of the leads convert. This means that the model observed 3 true predictions and 2 false predictions, equal to a recall of 60% (3/5).