# What do you understand by Recall and Precision?

What do you understand by Recall and Precision?

Precision and recall both are metrics used for understanding the performance of your classification algorithm.
In simple words
Precision: It tells you how many are records are classified correctly.
Recall: It tells you how well your algorithm is able to study the pattern in your data and upto what extent it can recall that.

Suppose in a village of 100 people, 30 people got infested with a rare virus, which starts showing visible signs in a month. You are a clinician and you decided to use a detector to identify infested people before it’s too late . Your device detected that 40 out of 100 people were infested with virus (Detected Positive). Your findings now mapped with actual cases as follows:

Precision and Recall of your detector can be understood by answering following questions.

Recall: What percentage of people actually infested with virus were detected correctly using your device? = 20/ 30 or 66.67%

Precision: What percentage of people detected positive using your device were actually infested with virus? = 20/ 40 or 50.00%