Recall is defined as the number of true positive (TP) predictions, divided by the sum of the number of true positives and false negatives (TP+FN): $$\text{Recall}=\frac{tp}{tp+fn} \, $$ It is also referred to as the True Positive Rate (TPR) or Sensitivity. See: http://en.wikipedia.org/wiki/Precision_and_recall Recall is defined only for a specific class value, and should thus be labeled with the class value for which is was computed. Use the mean_weighted_recall for the weighted average over all class values.
| Minimum value | 0 |
| Maximum value | 0 |
| Unit | |
| Optimization | Higher is better |