Timeline for Advantages of ROC curves
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Nov 21, 2019 at 18:28 | comment | added | user209249 | Beginners often have a hard time understanding these curves. Therefore, I wouldn't necessarily recommend to show it to consumers in order to advertise your product. I think, there you want something that is more simplistic. The curve is more than the individual points though. | |
| Nov 21, 2019 at 2:03 | comment | added | Frank Harrell | I seriously question that consumers and analysts can get insight from these curves that is anywhere near as intuitive as showing a calibration curve overlaid with a high-resolution histogram showing the predicted values. And each point on the ROC curve is an improper accuracy score. | |
| Nov 16, 2019 at 22:56 | comment | added | user209249 | There is way more information in these graphs than in a single one dimensional accuracy score. The same score can come from many different distributions. Do you have early recognition? Do you have multiple classes of positive samples that behave differently? Is your result statistically significant? All those questions can be obvious to answer by looking at those graphs and impossible to address with a single accuracy score. | |
| Nov 16, 2019 at 13:21 | comment | added | Frank Harrell | These graphs provide no insight and have an exceptionally high ink:information ratio IMHO. Stick with proper accuracy scores: fharrell.com/post/class-damage fharrell.com/post/addvalue | |
| Nov 15, 2019 at 22:53 | history | edited | user209249 | CC BY-SA 4.0 |
added 271 characters in body
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| Nov 15, 2019 at 22:37 | history | answered | user209249 | CC BY-SA 4.0 |