# Precision-Recall curve interpretation

When given an example confusion matrix:

TP = 5000 FP = 1000

FN = 0 TN = 100

This would result in a point on the PR-curve where the x coordinate is fixed [1, y]

--> thus perfect recall --> meaning zero false negatives

--> however this says nothing about true negatives and false positives

When given an example confusion matrix:

TP = 5000 FP = 0

FN = 10 TN = 100

This would result in a point on the PR-curve where the y coordinate is fixed [x, 1]

--> thus perfect precision --> meaning no false positives

--> however this says nothing about false negatives and true negatives

When analysing a ROC curve it's intuitive to maximise TPR and minimise FPR. But for the PR-curve one needs to maximise both. How does one know to maximise recall vs maximising precision? When is recall more important than precision? How should you trade-off these 2 metrics?

Recall: TP/(TP+FN) How can you assume anything regarding this metric when you are interested in finding TN's?

Precision: TP/(TP+FP) Is it ok to assume precision is less important when you want to find TN's and thus 'value' points that are on the top and bottom left less? --> because you don't mind having false positives as long as you find true negatives.