Timeline for ROC-AUC curve as metric for binary classifier without machine learning algorithm
Current License: CC BY-SA 3.0
17 events
| when toggle format | what | by | license | comment | |
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| S Sep 14, 2021 at 20:24 | history | suggested | Shayan Shafiq |
Added missing tags
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| Sep 14, 2021 at 0:22 | review | Suggested edits | |||
| S Sep 14, 2021 at 20:24 | |||||
| Sep 2, 2019 at 22:02 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
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| Apr 5, 2018 at 12:59 | history | edited | ocram | CC BY-SA 3.0 |
added 1 character in body
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| Apr 5, 2018 at 12:54 | answer | added | desertnaut | timeline score: 1 | |
| Apr 5, 2018 at 12:04 | comment | added | TwinPenguins | The way you explained it sounded like you have a 1-dimensional feature space and you want to do binary classification based on that. If this as simple, you can call it if you like you do not need machine learning. In principle, "machine learning" becomes obvious that you have more than 1 feature that you have to make decisions, and human can not figure out the relationship exists between all features to the target (here binary classification), and use the trained model to use for future prediction on unseen data with the same feature set. Hope this helps. | |
| Apr 5, 2018 at 9:42 | history | asked | ocram | CC BY-SA 3.0 |