Comments (1)
The algorithm is based on big data. For example, the difficulty is calculated from the probability of recall of the first review. It requires that the same contents are reviewed by a group of learners.
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Related Issues (20)
- Inclusion of any of the boosting models HOT 23
- Remove collecitons of people who misuse Hard from the calculation of the default parameters HOT 4
- [TODO] Improve Ebisu HOT 8
- 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
- [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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