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What should be the ideal value of precision and recall?
In information retrieval, a perfect precision score of 1.0 means that every result retrieved by a search was relevant (but says nothing about whether all relevant documents were retrieved) whereas a perfect recall score of 1.0 means that all relevant documents were retrieved by the search (but says nothing about how …
What is the ratio of the number of relevant documents retrieved to the total of relevant documents retrieved?
Precision
Precision is defined as the ratio of the number of relevant and retrieved documents(number of items retrieved that are actually useful to the user and match his search need) to the number of total retrieved documents from the query.
Which measure is used to indicate how many of the total relevant documents are actually retrieved by the IR system?
Recall
Recall. Recall is the fraction of the documents that are relevant to the query that are successfully retrieved. In binary classification, recall is often called sensitivity. So it can be looked at as the probability that a relevant document is retrieved by the query.
What is the difference between recall and precision?
Let us look at the formulas of precision and recall for a better understanding. Recall = Number of pages that were retrieved and relevant / Total number of relevant pages. Precision = Number of pages that were retrieved and relevant / Total number of retrieved pages.
How to calculate the recall curve for precision?
Finally, precision = TP/ (TP+FN) = 4/7 and recall = TP/ (TP+FP) = 4/6 = 2/3. This means when the precision is 4/7, the recall is 2/3. By setting different thresholds, we get multiple such precision, recall pairs. By plotting multiple such P-R pairs with either value ranging from 0 to 1, we get a PR curve.
What’s the difference between precision and relevant pages?
Precision = Number of pages that were retrieved and relevant / Total number of retrieved pages. You can see that the numerator in both cases is the same, only the denominator changes. Also, this definition of precision is specific to information retrieval, and is different from the statistical definition of precision.
Why are precision and recall needed in search engines?
Clearly, this search engine is not performing well because it is simply returning every page that exists. But if we ignored precision and only stated the recall = 100%, it would seem like the system is doing really well. So as you can see, we need both precision and recall to evaluate a search engine or any information retrieval task.