Comments (9)
Why not use the variance of p? Cheating algorithms tend to provide a small range of predictions, so the variance would be small, too.
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I think my measure is more intuitive. You can add both if you want to, you'll have to re-run the benchmark anyway.
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I find out a typical cheating case:
When the algorithm is measured by itself, FSRS-4.5 is worse than DASH[ACT-R]. But when they are measured by each other, FSRS-4.5 is better than DASH[ACT-R]. Anyway, the log loss is uncheatable.
Here are their calibration graphs.
FSRS-4.5:
DASH[ACT-R]:
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Yes, but we have to do this for all 20 000 collections and compare averages. We can't deicde whether an algorithm is cheating or not based on one collection.
I would suggest implementing both my original suggestion and your stdev of p.
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The main problem is we don't know the real distribution of retrievability. The ideas of you and me both assume that the real distribution is more flat than the distribution predicted by a cheat algorithm.
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True. Well, do you think DASH[ACT-R] should be included in the table now?
EDIT: I think it's reasonable to assume that the true distribution is a beta distribution.
The thing is, beta distribution can look like what we're seeing with ACT-R, depending on alpha and beta. We could also add UM (as the third metric), with FSRS-4.5 as comparison, but that would be difficult to interpret.
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Btw, don't forget about #55
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Well, do you think DASH[ACT-R] should be included in the table now?
If we still rank models by RMSE(bins), I tend not to include it. If we rank models by log loss, I will include it.
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Hmmm. Ok, let's sort by log-loss then.
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Related Issues (20)
- Inclusion of any of the boosting models HOT 23
- [Feature Request] Add a Transformer HOT 15
- collect bad cases from Anki users' dataset HOT 9
- visualize metrics over time HOT 2
- [Feature Request] Train a gradient-boosted decision tree HOT 36
- Some weird first forgetting curves HOT 11
- [Feature request] Add confidence intervals for all metrics HOT 9
- accidental post
- Revlogs parsing HOT 12
- [Question] A βrawβ version of the tiny_dataset.zip HOT 3
- [Feature Request] Add a BiLSTM HOT 2
- [Feature request] Add the ACT-R model (see paper) HOT 21
- [TODO] Add DASH and its variants HOT 13
- Write an article about binned RMSE and cheating calibration metrics HOT 7
- Ebisu? HOT 7
- [Question] Some more details from a ML perspective HOT 8
- Cannot download dataset from huggingface HOT 4
- Neural network scheduler HOT 42
- Add MCC
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