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What is Recall?

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Recall measures the proportion of actual observations that belong to the positive class that were correctly classified by the algorithm. In other words, it measures how sensitive the algorithm is in detecting true positives. In general,  

Recall = True Positives / (True Positives + False Negatives)

Using an example:

Confusion Matrix

Recall = 150 / (150 + 50) = .75

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