One Punch Dan （一拳丹）

  What are the precision & recall and why are they used?  For binary classification problems, it's usually a matter of how to differentiate from a negative class to a positive class. When we have a binary classifier to make predictions, there will always be true positives (TP) , false positives (FP) , true negatives (TN) , and false negatives (FN) . The classifier that can produce more true positives and negatives (less false positives and negatives in other words) indicates better predictive power and less business cost. As a data scientist, I usually first define the cost of a false negative and the cost of a false positive from a business standpoint, so that I can optimize the threshold accordingly and balance the trade-off. In a nutshell, both precision and recall are the metrics to evaluate the predictive performance. Below are the detailed definitions about precision and recall. precision = TP/(TP + FP) , it tells how sure you are of your true positives. For exa...