Glossary:R-Metric

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 17, R-Metric is used solely to compare Algorithm SM-15 (known from SuperMemo 16) and the new Algorithm SM-17. It is shown as percentage in Statistics and Tools : Statistics : Analysis : Use : Efficiency : R-Metric. R-Metric is a difference between the performance of the two algorithms: R-Metric=LSRM(Alg-17)-LSRM(Alg-15), where LSRM is the least squares predicted recall measure for a given algorithm. R-Metric greater than zero shows superiority of Algorithm SM-17. 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-17, 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)

Example:

File:Daily Algorithm SM-17 performance metric.jpg

SuperMemo 17 performance metric