Precision and recall are ways of monitoring the power of machine learning implementation. But they often used at the same time.
Precision answers the question, “Out of the items that the classifier predicted to be relevant, how many are truly relevant?”
Whereas, recall answers the question, “Out of all the items that are truly relevant, how many are found by the classifier?
In general, the meaning of precision is the fact of being exact and accurate. So the same will go in our machine learning model as well. If you have a set of items that your model needs to predict to be relevant. How many items are truly relevant?
The below figure shows the Venn diagram that precision and recall.
Mathematically, precision and recall can be defined as the following:
precision = # happy correct answers/# total items returned by ranker
recall = # happy correct answers/# total relevant answers