Timeline for Choice Between Alternatives in Machine Learning
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
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| May 8, 2022 at 14:45 | comment | added | Cory Smith | Your vote for this metric & feature set is helpful & if you put it in an answer with additional logic I will accept it. Thank you. In particular, if you would say more about why you prefer the absolute metric to the relative one I proposed, that is getting at the question. I don't think it's right to say that we can't have any handle on the alternative choices and this means ignoring features. Losers' features may (or may not) have contextual value. Even a simple model y = x+1 makes a prediction for x=2.3 even if we don't see x=2.3 itself. | |
| May 8, 2022 at 11:23 | comment | added | J. Delaney | The point is that you have no way of knowing if a given choice is correct or not because you don't know what the teams' performance would have been had the other candidate been chosen. The only relevant metric I can see here is the accuracy of the prediction given the characteristics of the leader | |
| May 8, 2022 at 4:55 | history | edited | Cory Smith | CC BY-SA 4.0 |
added 459 characters in body
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| May 8, 2022 at 4:29 | comment | added | Cory Smith | Let me know if that's helpful. Part of my question, per above, would simply be the most appropriate metric for assessing model accuracy. When testing about including the loser's characteristics, should I assess accuracy based on the final prediction? or should I assess based on what the model says about the difference between choosing A or B (since the relative choice is all that matters)? | |
| May 8, 2022 at 4:15 | comment | added | Cory Smith | The losers' characteristics ultimately might not be predictive. However, by telling the model about both options, it might learn what was relevant in the change (how A was different from B) and what was simply inevitable (characteristics A & B share). A & B are arbitrary labels not types, each one (in this example) is an individual. | |
| May 6, 2022 at 18:56 | answer | added | Tim | timeline score: 3 | |
| May 6, 2022 at 18:10 | comment | added | J. Delaney | It's not clear why you think that the features of the loser should be relevant for your model - assuming the performance of each team depends on the characteristics of it's leader, why does it matter who was not chosen ? The meaning of the "A" and "B" labels is also not clear, is that just random labeling or are there candidates of "type A" and "type B" ? | |
| S May 6, 2022 at 17:44 | review | First questions | |||
| May 6, 2022 at 20:04 | |||||
| S May 6, 2022 at 17:44 | history | asked | Cory Smith | CC BY-SA 4.0 |