Timeline for Question about computing Bayes Error - with or without loss function?
Current License: CC BY-SA 3.0
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| Feb 10, 2014 at 11:44 | comment | added | Accidental Statistician | I think so, yes. If you have a 1-0 loss function, then the loss is minimised by choosing the most likely class every time. In other words, you're minimising P(error|x) for all x, which just gives you P(error). In other words, if your loss function is chosen as the chance of you being wrong, it minimises the chance of you being wrong! | |
| Feb 10, 2014 at 6:06 | comment | added | user39663 | Thanks a lot. Meanwhile I did some further reading, and I guess I was a little bit confused about the term "Bayes Error". When I understand correctly, P(errror|π) would give me the error given my decision policy (which includes the loss function), and P(error) is just a theoretical scenario that describes the best possible classification accuracy we can achieve. And I assume if we have a 1-0 loss function, P(error|π) would be equal to P(error)? | |
| Feb 10, 2014 at 6:03 | vote | accept | CommunityBot | ||
| Feb 10, 2014 at 0:35 | history | answered | Accidental Statistician | CC BY-SA 3.0 |