looking behind accuracy precision and recall let us step back and think again what we are trying to achieve here actuallywe do not need classifier that perfectly predicts good and bad answersas we measured it until now using accuracy if we can tune the classifier to be particularly good in predicting one classwe could adapt the feedback to the user accordingly if we had classifierfor examplethat was always right when it predicted an answer to be badwe would give no feedback until the classifier detected the answer to be bad contrariwiseif the classifier succeeded in predicting answers to be always goodwe could show helpful comments to the user at the beginning and remove them when the classifier said that the answer is good one to find out which situation we are in herewe have to understand how to measure precision and recall to understand thiswe have to look into the four distinct classification results as they are described in the following tableclassified as positive in reality it is negative positive true positive (tpfalse negative (fnnegative false positive (fptrue negative (tnfor instanceif the classifier predicts an instance to be positive and the instance indeed is positive in realitythis is true positive instance if on the other handthe classifier misclassified that instance saying that it is negative while in reality it is positivethat instance is said to be false negative what we want is to have high success rate when we are predicting post as either good or badbut not necessarily both that iswe want as many true positives as possible this is what precision captures