metric

False discovery rate is a metric that answers the question, "How many positive predictions are incorrect?".

Precision is a metric that answers the question, "How many positive predictions are correct?".

Recall - or sensitivity, or True Positive Rate - is a metric that answers the question, "How many positive example are predicted by the model?".

Specificity - or True Negative Rate - is a metric that answers the question, "How many negative examples are correctly predicted by the model?"

Accuracy is a metric that answers the question "How many predictions are correct?".