Evaluation 11: interpolated recall-precision plot

Recall-precision graphs are the standard way to compare search algorithms. To construct a standard recall-precision graph, we interpolate precision values, and average them over a large set of queries. The standard interpolation strategy is based on the assumption that precision always decreases as recall grows.

[@lomoyang3034]

Thanks very much, Victor! Your explanation with your graph is really really great! In my opinion, better than the explanation from my professor!

[@Tukiopuuyt]

This is so helpful. Thank you! You manage to clarify something that is very opaque in my textbook.

[@haidersyed88]

Brilliant explanation!

[@chu-yunwang1079]

Hi Victor thank you so much for this video
IT IS AWESOMELY HELPFUL

[@rosj91]

Nice explanation! Thanks a lot!

[@iamhumanbeing7]

Thank you for the video!

[@aicoding2010]

Thanks for the video. We dont have recall = 0, why we interpolated precision at recall = 0?

[@billelaklouche6991]

Thanks a lot

[@ankurgupta2806]

from which course is the video taken can you please tell

[@thekiller9710]

And here I was thinking that finding a resource for normal humans is impossible to find on this topic

[@nalinsharma7273]

thanks severus sanpe

[@cyrusmaher169]

Hey Victor! Hope all is well :)

[@jadeite20]

it says 11 standard but you only do for 10. 0.1,...,1.0. Why do you skip that important part? The standard needs to be done for 11 states.