Timeline for Is there such a thing like the machine learning paradox?
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 14, 2017 at 17:40 | comment | added | DukeZhou | That said, I'm finding the explanation in the O'Reilly article hard to follow, although there are some good points overall regarding the ethics. I can't see support in his article that 100% accuracy in a test set is categorically sub-optimal, as opposed to merely irrelevant, although he does later mention manipulating the data set to achieve 100% accuracy, which is, obviously, problematic. | |
| Jun 14, 2017 at 17:19 | comment | added | DukeZhou | The concept of a solved game may be useful. If you have 100% accuracy, the problem is tractable, which is rarely the case in highly complex systems. ML is proving useful in tackling intractable problems, but in a condition of intractability, here a function of bounded rationality, you never have perfect certainty, only perceived optimality. The ML system may even get it right 100% of the time in a given sample, but that is no guarantee it will always do so. | |
| Jun 12, 2017 at 11:00 | vote | accept | Alpha | ||
| Jun 11, 2017 at 20:11 | answer | added | Pak | timeline score: 2 | |
| Jun 11, 2017 at 18:52 | history | edited | Alpha | CC BY-SA 3.0 |
added 566 characters in body
|
| Jun 11, 2017 at 18:11 | answer | added | Shawn Mehan | timeline score: 3 | |
| S Jun 11, 2017 at 17:07 | history | suggested | Shawn Mehan | CC BY-SA 3.0 |
small edits.
|
| Jun 11, 2017 at 16:53 | review | Suggested edits | |||
| S Jun 11, 2017 at 17:07 | |||||
| Jun 11, 2017 at 10:23 | answer | added | user6933 | timeline score: 5 | |
| Jun 10, 2017 at 12:23 | history | asked | Alpha | CC BY-SA 3.0 |