# Glossary:R-Metric

Revision as of 19:37, 12 April 2019 by SuperMemoHelp (talk | contribs)

- R-Metric (Recall Metric)
- absolute measure of performance of two spaced repetition algorithms based on their ability to predict recall before a grade is scored. In SuperMemo 18,
**R-Metric**is used solely to compare Algorithm SM-15 (known from SuperMemo 16) and the new Algorithm SM-18. It is shown as percentage in**Statistics**and**Toolkit : Statistics : Analysis : Use : Efficiency : R-Metric**.**R-Metric**is a difference between the performance of the two algorithms:`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)