Timeline for Interpretation of plots from neural network
Current License: CC BY-SA 4.0
11 events
when toggle format | what | by | license | comment | |
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Jan 4, 2022 at 19:44 | answer | added | Jay Ekosanmi | timeline score: 1 | |
Jan 4, 2022 at 19:37 | comment | added | Sycorax♦ | It's not necessarily the kind of subjective question that is discouraged on Stack Exchange, but it does make the question incomplete. Knowing the answer to "Does my neural network fit well?" requires information that only you have: "Do the predictions from this model solve my problem?" | |
Jan 4, 2022 at 19:32 | comment | added | silent_hunter | So I think that this opinion is so general and don't give answer about above question | |
S Jan 4, 2022 at 19:29 | review | Reopen votes | |||
Jan 4, 2022 at 19:31 | |||||
S Jan 4, 2022 at 19:29 | history | reopened |
Avraham Sycorax♦ |
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Jan 4, 2022 at 19:25 | history | left closed in review | whuber♦ | Original close reason(s) were not resolved | |
Jan 4, 2022 at 19:23 | review | Reopen votes | |||
Jan 4, 2022 at 19:25 | |||||
Jan 4, 2022 at 19:22 | history | edited | silent_hunter | CC BY-SA 4.0 |
added 91 characters in body
Added to review
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Jan 4, 2022 at 19:20 | history | closed | Sycorax♦ | Needs details or clarity | |
Jan 4, 2022 at 19:18 | comment | added | Sycorax♦ | I think you forgot to attach the plots that you're referring to. In any event, machine learning and statistical models don't exist in a vacuum. The relevant question isn't "is my loss value good?", but instead "is my model good enough for my particular application?" When the model makes mistakes (which it will, inevitably), you want to know if the costs of those mistakes are outweighed by the value of its correct predictions. | |
Jan 4, 2022 at 19:16 | history | asked | silent_hunter | CC BY-SA 4.0 |