Timeline for Is it reasonable that overfitted model be bettern than non-overfitted model?
Current License: CC BY-SA 3.0
5 events
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| Aug 16, 2017 at 7:55 | comment | added | rinspy | @Aaron Isn't "mismatch between the training and validation data" what we call "noise"? I.e. it is still the same overfitting, it's just that increasing the number of samples will not increase the signal-to-noise ratio. See also this answer: stats.stackexchange.com/questions/274952/… | |
| Aug 15, 2017 at 17:00 | comment | added | Aaron | There can be other reasons for why there is a gap between training and validation errors. For example, there can be a mismatch between the training and the validation data. | |
| Aug 15, 2017 at 16:44 | comment | added | rinspy | By that definition, you do have overfitting but it's not necessarily a problem :) | |
| Aug 15, 2017 at 16:41 | comment | added | Hossein | Thanks for your response. I think another definition of overfitting is when there are a significant difference between training and validation errors. By this definition, I have the overfitting problem. | |
| Aug 15, 2017 at 16:35 | history | answered | Aaron | CC BY-SA 3.0 |