A measure of model performance that shows how well the system is properly classifying data, calculated as the number of times that the system classified data into a particular positive class divided by all the times the model was supposed to classify data into that class (true positives divided by the sum of true positives and false negatives). Lower recall means the model is predicting too many false negatives. Recall is also known as Sensitivity. A recall / sensitivity measure of 1 means that the model has detected all the available true positives without missing any that it has misclassified as negative.
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