Skip to main content
Data Science

You are not logged in. Your edit will be placed in a queue until it is peer reviewed.

We welcome edits that make the post easier to understand and more valuable for readers. Because community members review edits, please try to make the post substantially better than how you found it, for example, by fixing grammar or adding additional resources and hyperlinks.

Required fields*

Cancel
7
  • $\begingroup$ I am not sure I understand what the problem is. Bias of an estimator is an expected value not instance value. For example I am not sure why pred1 is preferable over pred2, is it supposed to sum to some constant? If so, state this constraint clearly. $\endgroup$ Commented May 31, 2022 at 13:33
  • $\begingroup$ In other words state all the constraints that should be satisfied clearly $\endgroup$ Commented May 31, 2022 at 13:35
  • $\begingroup$ > Bias of an estimator is an expected value not instance value. You are right about that, I shouldn't mix that up. My problem is the model being off significantly when summing up multiple predictions. <hr> >In other words state all the constraints that should be satisfied clearly Thank you, I edited the question to include why pred1 is better than pred2. $\endgroup$ Commented May 31, 2022 at 17:46
  • $\begingroup$ Honestly I still fail to understand what is the required condition that has to be met. For example rounding the predictions both pred1 and pred2 are identical. Please clarify what do you mean by summing and what the expected result should be. $\endgroup$ Commented May 31, 2022 at 18:05
  • $\begingroup$ Maybe what you really need is rounding the predictions to some fixed accuracy instead of different loss functions $\endgroup$ Commented May 31, 2022 at 18:10