1 Matching Annotations
- May 2017
Precision: It is a measure of correctness achieved in positive prediction i.e. of observations labeled as positive, how many are actually labeled positive. Precision = TP / (TP + FP) Recall: It is a measure of actual observations which are labeled (predicted) correctly i.e. how many observations of positive class are labeled correctly. It is also known as ‘Sensitivity’. Recall = TP / (TP + FN)
Example: In cancer research you may want higher recall, Since you want all actual positive observations to classified as True Positive. A lower Precision maybe alright because some healthy people classified as cancerous can be rectified later.