Precision and Recall
Precision and recall are two measures of a classifier's quality: precision is the share of its positive predictions that are correct, and recall is the share of actual positives it finds.
What is Precision and Recall?
A fraud filter with high precision rarely flags honest customers; one with high recall catches most fraud. Improving one usually worsens the other, so the right balance depends on the cost of each kind of error. The F1 score combines both.
How it works
Precision equals true positives divided by all predicted positives; recall equals true positives divided by all actual positives.
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