# Can Precision and Recall be equal to 100% at the same time?

**URL:** <https://community.deeplearning.ai/t/can-precision-and-recall-be-equal-to-100-at-the-same-time/194700>\
**Category:** Advanced Learning Algorithms\
**Tags:** week-module-3\
**Created:** [August 29, 2022, 1:15am UTC](https://community.deeplearning.ai/t/can-precision-and-recall-be-equal-to-100-at-the-same-time/194700 "2022-08-29T01:15:41Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![Robert\_Ascan](https://avatars.discourse-cdn.com/v4/letter/r/bc8723/32.png) [@Robert\_Ascan](https://community.deeplearning.ai/u/Robert_Ascan)\
**Post date:** [August 29, 2022, 1:15am UTC](https://community.deeplearning.ai/t/can-precision-and-recall-be-equal-to-100-at-the-same-time/194700/1 "2022-08-29T01:15:41Z")

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Do I understand it correctly that, theoretically, both Precision and Recall can be 100% at the same time? This can be the case when we got a perfect algorithm that produces no False Positives or False Negatives. The graph in the video “Trading off precision and recall” at 5:38, however, does not seem to allow for such a scenario!?

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**Author:** ![TMosh](https://sea1.discourse-cdn.com/dlai/user_avatar/community.deeplearning.ai/tmosh/32/9321_2.png) [@TMosh](https://community.deeplearning.ai/u/TMosh)\
**Post date:** [August 29, 2022, 1:23am UTC](https://community.deeplearning.ai/t/can-precision-and-recall-be-equal-to-100-at-the-same-time/194700/2 "2022-08-29T01:23:17Z")

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Yes. It’s just extremely unlikely.

If you plug 100% into this equation for both P and R, you get an F1 score of 1.0.

![image](https://global.discourse-cdn.com/dlai/original/3X/f/a/fad4dfe3dbf49b4ba748007179e886271088c4c9.png)

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**Author:** ![rmwkwok](https://sea1.discourse-cdn.com/dlai/user_avatar/community.deeplearning.ai/rmwkwok/32/126977_2.png) [@rmwkwok](https://community.deeplearning.ai/u/rmwkwok)\
**Post date:** [August 29, 2022, 2:58am UTC](https://community.deeplearning.ai/t/can-precision-and-recall-be-equal-to-100-at-the-same-time/194700/3 "2022-08-29T02:58:28Z")

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Hello @Robert_Ascan , a graph like that represents the performance of _ **a** _ model. _ **That** _ model does not allow such scenario and it is not a perfect model, as you said, that can produce no False Postiives or False Negatives regardless of the threshold used. The graph is a typical one but not universally true.