What would you care more about – precision or recall for spam filtering problem?

To understand precision and recall, it is important to know about False positives(FP) : mail was NOT a SPAM but it WAS LABELLED as spam False negatives(FN): mail WAS a SPAM but was NOT LABELLED as spam True positives(TP): mail WAS a SPAM and also LABELLED as spam True negatives(TN): mail was NOT a SPAM and also LABELLED as…

You have come up with a Spam classifier. How do you measure accuracy ?

Spam filtering is a classification problem. In a classification problem, the following are the common metrics used to measure efficacy : True positives : Those data points where the outcome is spam and the document is actually spam. True Negatives: Those data points where the outcome is not spam and the document is actually not…