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In the question of What is the relationship between the accuracy and the loss in deep learning?, @Jérémy Blain gave a fantastic interpretation of 'relationship' between accuracy and loss:

  • 1 - low accuracy and big loss means you made huge errors on a lot of data
  • 2 - low accuracy but small loss means you made little errors on a lot of data
  • 3 - high accuracy with small loss means you made low errors on a few data (best case)
  • 4 - high accuracy but a big loss, means you made huge errors on a few data.

As I understand,

  • 1 - implies bad algorithm 'in most of time'
  • 2 - implies overfitting
  • 3 - is what we want

Questions are:

 1   Any other reasons caused 1 and 2?
 2   what will lead to case 4 : high accuracy but a big loss?
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  • $\begingroup$ Regarding your question no.2 - it means that the model is very good, well predicts almost all instances, but there are some outliers of a huge impact on the loss. $\endgroup$
    – Grzegorz Sionkowski
    Commented Dec 16, 2019 at 10:47
  • $\begingroup$ @GrzegorzSionkowski,thanks,outliers will be big reason for high acc but big loss. $\endgroup$
    – Alex Luya
    Commented Dec 16, 2019 at 11:35

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