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DOC Improve plot_precision_recall
#28967
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Thanks for the PR. Here is a quick suggestion but otherwise LGTM!
measure of result relevancy, while recall is a measure of how many of the | ||
relevant results are returned. 'Relevancy' here refers to items that are | ||
postively labeled, true positives and false negatives. |
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I think we can avoid introducing the word "relevancy" and more directly state:
measure of result relevancy, while recall is a measure of how many of the | |
relevant results are returned. 'Relevancy' here refers to items that are | |
postively labeled, true positives and false negatives. | |
measure of the fraction of relevant items among actually returned items while recall | |
is a measure of the fraction of items that were returned among all items that should | |
have been returned. |
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Thanks!
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Thanks for addressing this issue @lucyleeow. Here is just a nit but otherwise LGTM.
both high recall and high precision, where high precision relates to low | ||
false positives in returned results, and high recall relates to a low false negatives | ||
in relevant results. High scores for both show that the classifier is returning |
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I think "fewer" is more gramatically correct in this context than "low" false positives/negatives. What do you think of a phrasing:
High precision corresponds to fewer false positives in returned results, and high recall corresponds to fewer false negatives in relevant results.
I also feel that the word "relates" is a bit vague. We can alternatively say something similar to:
High precision can be achieved by having few false positives in the returned results, and high recall can achieved by having few false negatives in the relevant results.
Reference Issues/PRs
closes #18719
What does this implement/fix? Explain your changes.
Any other comments?
Happy to change wording.