From supermemo.guru
Jump to navigation Jump to search

R-metric (for Recall Metric) is the absolute measure of performance of two spaced repetition algorithms based on their ability to predict recall before a grade is scored. In SuperMemo, R-Metric is used to compare Algorithm SM-15 (good benchmark perfected for 20 years of use) and the newest algorithm currently in use. R-metric is shown as percentage in SuperMemo for Windows in Statistics and Toolkit : Statistics : Analysis : Use : Efficiency : R-Metric. R-Metric is a difference between the performance of the two algorithms. For example: R-Metric=LSRM(Alg-15)-LSRM(Alg-18), where LSRM is the least squares predicted recall measure for a given algorithm. R-Metric greater than zero shows superiority of Algorithm SM-18. R-Metric less than zero indicates underperformance of the new algorithm. LSRM is a square root of the average of squared absolute differences in recall predictions: abs(Recall-PredictedRecall), where Recall is 0 for failing grades and Recall is 1 for passing grades. PredictedRecall is a prediction issued by the algorithm before the repetition. In Algorithm SM-18, the prediction is a weighted average of the value taken from the Recall[] matrix, and R (retrievability) computed from S (stability) and the used interval. The weight used is based on prior repetition cases which inform of the significance of the Recall[] matrix prediction (the prediction becomes more meaningful with more prior repetition data)

See also: Universal metric

This glossary entry is used to explain SuperMemo, a pioneer of spaced repetition software since 1987