Comments (6)
Oh wait, I just realized that FFTKDE supports weighting. So we can calculate the mode more accurately by using the square root of the number of reviews.
Note that this time I decided not to sort the values, so instead the order is HRM, HSM, KDE:
[0.3534, 0.3601, 0.5294]
[0.9, 0.9, 0.9759]
[2.3, 2.5861, 2.5091]
[10.9, 10.9, 365.0]
[5.0491, 5.049, 5.0304]
[1.0598, 1.0597, 0.1]
[0.7406, 0.86, 0.8587]
[0.0, 0.0, 0.0]
[1.49, 1.49, 1.4894]
[0.1, 0.1, 0.1]
[0.94, 0.94, 0.9414]
[2.1257, 2.18, 2.1809]
[0.01, 0.01, 0.01]
[0.34, 0.34, 0.3397]
[1.26, 1.26, 2.0]
[0.0, 0.0, 0.0]
[2.61, 2.61, 4.0]
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So here's the final result:
[0.3568, 0.9, 2.5476, 10.9, 5.049, 1.0598, 0.8594, 0.0, 1.49, 0.1, 0.94, 2.1804, 0.01, 0.34, 1.26, 0.0, 2.61]
It's pretty clear that the mode is affected by the default value of a parameter a lot. So I think it's necessary to exclude all values that are equal to their defaults. For example, say S0 is 10.9 for some user. That's the default value. So we need to remove it before calculating the mode.
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Here are the new best modes, after excluding users who have 1 (or more) parameter that is exactly equal to it's default value:
[0.5738, 1.4817, 2.3917, 6.1572, 5.049, 1.0598, 0.7226, 0.0, 1.3714, 0.1, 0.8204, 2.1257, 0.01, 0.2692, 1.1412, 0.0, 2.7572]
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Slightly unrelated. but I noticed that w[12] is distributed pretty much uniformly. All other parameters follow some sort of non-uniform distribution.
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I find that w[15]'s mode is zero. It's a signal that many users press hard when they should press again, because the stability didn't increase (if we didn't clamp it, it could be negative).
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We could allow SInc for "Hard" to be less than 1, in other words, allow stability to decrease, but that will likely lead to a new type of Ease Hell, where the user keeps pressing "Hard" and the intervals get shorter and shorter.
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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 9
- [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
- [Feature request] A quantitative measure of cheating HOT 9
- 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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