What is the difference between precision and recall?
Question
Define precision and recall.
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Machine Learning
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Answers ( 2 )
Precision is the ratio of several events you can correctly recall to the total number of events you recall (mix of correct and wrong recalls).
Precision = (True Positive) / (True Positive + False Positive)
A recall is the ratio of a number of events you can recall the number of total events.
Recall = (True Positive) / (True Positive + False Negative)
Precision: Out of the total predicted positives by the model, how many are actually positive.
Precision = True Positive/ True positive + False positive
Recall: Out of the total actual positive in the datset, how many positives are correctly predicted by the model
Recall: True Fositive/ True positive + False Negative