Comments (3)
It's not a bug. The optimizer doesn't use RMSE as the loss function. And it's not guaranteed that the optimizer could find the global minimum.
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Just use the parameters whose RMSE is lower.
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Thank you for chiming in. This raises two questions for me:
- If I achieve a lower RMSE through manual tweaking compared to what the optimizer achieves, should I prefer the former or the latter?
- If I further tweak the optimized parameters manually and achieve an even lower RMSE, is it advisable to prefer it or should I still go with the originally optimized parameters?
P.S. I've removed [BUG] from the title but I think I'm not able to remove the label itself.
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Related Issues (20)
- [Question] Date change HOT 6
- [Question] Does fsrs deals well with cramming HOT 1
- [Question] HOT 21
- Optimiser Stops Working After a Single Review HOT 23
- [Question]: Questions regarding Optimisation HOT 3
- [Question] Should I use a low value produced by optimal retention (75%)? HOT 6
- [Question] Can FSRS schedule intervals shorter than a day? HOT 8
- [Question] What is the correct way to determine the card state (New, Learning, Review, Relearning) when implementing the algorythm separately from Anki? HOT 2
- [BUG]: Intervals don't update after changing Desired Retention HOT 12
- [BUG] "Ignore reviews before" Not Working HOT 31
- [Feature Request] Uneasy day
- [Feature Request] Decreasing load balance
- [Question] Wiki—post-lapse stability example parameter inconsistency? HOT 1
- [Question] Do the stability and difficulty not change when the card is in `Learning` state? HOT 1
- [Question] HOT 3
- [Question] Confusion about short term scheduling HOT 10
- Improve Handling of 'Set due date' HOT 45
- [Question] HOT 5
- [Question] Very long initial intervals when using multiple card types HOT 3
- [Other] Let's use the FSRS Simulator to figure out how to implement Load Balancer HOT 59
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