Precision,Recall and F-score

I am working on a developing a new system which is based on information retrieval concept. My system retrieve's pdf and ppt files of research articles from the web. When i calculated precision,recall and f-score of the system, i had arrived at doubts.. I want to clarify that from the group members. The doubt is will ther开发者_Go百科e be a huge different between precision,recall and f-score. Because i computed precision to some 0.913 and recall goes very low like 0.3234 and f-score is about 0.4323 etc. Will it be possible?? I mean will precision and recall have this much huge difference or i calculated them wrongly.!! Please provide your suggestions as well your reference to some notes.. Thanks..

This is very possible - you can have low precision and high recall and vice versa.

For example, if you return the whole database, you will have 100% recall, but very low precision.

In your case, it means you are not returning very much of "false" data (all of what you are returning is "true"), but you are forgetting to return 70% of the data.

When we measure the accuracy of any trained classifier using metrics such as Recall and Precision, there is a possibility that these values will be different either in large/small amount.

Recall is measured as TP/(TP+FN), that is recall deals with False negatives.

Precision is measured as TP/(TP+FP), that is precision deals with False positives.

So the difference in Recall and Precision depends on FP and FN.

Low recall and high precision is very common. It just means that the classifier is very conservative - does not risk too much in saying that a sample is Positive (low recall), and thus when it does, it is very confident about it (high precision).