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*).

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