What I know? Firstly,
What book says?
A model that declares every record has high recall but low precision.
I understand that if predicted positive is high, precision will be low. But how will recall be high if predicted positive is high.
A model that assigns a positive class to every test record that matches one of the positive records in the training set has very high precision but low recall.
I am not able to properly comprehend how there is inverse relation between precision and recall.
Here is a doc that I found but I could not also understand from this doc as well.