Timeline for Inverse Relationship Between Precision and Recall
Current License: CC BY-SA 4.0
29 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 12, 2019 at 12:17 | vote | accept | tkarahan | ||
| Apr 12, 2019 at 12:16 | comment | added | tkarahan | Thank you so much. It really helped to clarify subject. | |
| Apr 12, 2019 at 12:15 | comment | added | Esmailian | @TolgaKarahan Exactly. | |
| Apr 12, 2019 at 12:12 | history | edited | Esmailian | CC BY-SA 4.0 | Explanation improved |
| Apr 12, 2019 at 12:09 | comment | added | tkarahan | It's good. Finally If I define TP / P as positive recall and TN / N as negative recall then I suppose with increasing precision I increase negative recall and with increasing recall because it is same thing I also increase positive recall. So it looks like matter of increasing negative or positive recall and which one more important to me. | |
| Apr 12, 2019 at 11:59 | comment | added | Esmailian | @TolgaKarahan Aha. For better-than-random models, increase in precision means decrease in recall (and vice versa), which is decrease in TP/P (P = TP+FN). For TN/N, we know when threshold is increased (decrease in recall) both TP and FP decrease since we are selecting less positives, thus FP/N decreases, and 1 - FP/N = TN/N increases. So the answer to your question is yes. | |
| Apr 12, 2019 at 11:47 | comment | added | tkarahan | I mean ratio of correctly predicted labels to all labels for a specific class. Like TP / P or TN / N. If I increase precision does it predict negative examples more accurately with increasing TN / N? | |
| Apr 12, 2019 at 11:24 | history | edited | Esmailian | CC BY-SA 4.0 | Caption corrected |
| Apr 12, 2019 at 11:15 | comment | added | Esmailian | @TolgaKarahan First we need to define "more accurately" in terms of TN, TP, etc. For example "accuracy" is for both positives and negatives, i.e. (TP+TN / P+N) which I added it to the plots, it has a rise and a fall for better-than-random models. | |
| Apr 12, 2019 at 11:12 | history | edited | Esmailian | CC BY-SA 4.0 | Explanation improved |
| Apr 12, 2019 at 8:13 | comment | added | tkarahan | So when random phenomena completely rules, in practice it is observed that they generally have inverse relationship. There are different situations but, can we say generally if we increase precision it means that we predict negative examples more accurately and if we increase recall it means that we predict positive examples more accurately? | |
| Apr 12, 2019 at 1:27 | history | edited | Esmailian | CC BY-SA 4.0 | Explanation improved |
| Apr 12, 2019 at 0:59 | history | edited | Esmailian | CC BY-SA 4.0 | Explanation improved |
| Apr 12, 2019 at 0:33 | history | edited | Esmailian | CC BY-SA 4.0 | Explanation improved |
| Apr 12, 2019 at 0:02 | history | edited | Esmailian | CC BY-SA 4.0 | Code corrected |
| Apr 11, 2019 at 23:56 | history | undeleted | Esmailian | ||
| Apr 11, 2019 at 23:56 | history | edited | Esmailian | CC BY-SA 4.0 | Explanation improved |
| Apr 11, 2019 at 22:34 | history | deleted | Esmailian | via Vote | |
| Apr 11, 2019 at 19:47 | history | edited | Esmailian | CC BY-SA 4.0 | Explanation improved |
| Apr 11, 2019 at 19:32 | history | edited | Esmailian | CC BY-SA 4.0 | Explanation improved |
| Apr 11, 2019 at 19:18 | history | edited | Esmailian | CC BY-SA 4.0 | added 34 characters in body |
| Apr 11, 2019 at 19:11 | history | edited | Esmailian | CC BY-SA 4.0 | Explanation improved |
| Apr 11, 2019 at 18:47 | history | edited | Esmailian | CC BY-SA 4.0 | Explanation improved |
| Apr 11, 2019 at 18:14 | history | edited | Esmailian | CC BY-SA 4.0 | Explanation improved |
| Apr 11, 2019 at 18:07 | history | edited | Esmailian | CC BY-SA 4.0 | Explanation improved |
| Apr 11, 2019 at 18:02 | history | undeleted | Esmailian | ||
| Apr 11, 2019 at 18:01 | history | deleted | Esmailian | via Vote | |
| Apr 11, 2019 at 17:59 | history | edited | Esmailian | CC BY-SA 4.0 | added 1 character in body |
| Apr 11, 2019 at 17:53 | history | answered | Esmailian | CC BY-SA 4.0 |