Search for you can get high recall but low precision

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Unformatted text preview: engine classifies each doc as “Relevant” or “Nonrelevant”   The accuracy of an engine: the frac)on of these classifica)ons that are correct Relevant Nonrelevant Retrieved tp fp Not Retrieved fn tn   Precision P = tp/(tp + fp)   Recall R = tp/(tp + fn)   (tp + tn) / ( tp + fp + fn + tn)   Accuracy is a commonly used evalua)on measure in machine learning classifica)on work   Why is this not a very useful evalua)on measure in IR? 11 12 2 Introduc)on to Informa)on Retrieval Sec. 8.3 Why not just use accuracy? Sec. 8.3 Precision/Recall   How to build a 99.9999% accurate search engine on a low budget…. Search for:   You can get high recall (but low precision) by retrieving all docs for all queries!   Recall is a non ­decreasing func)on of the number of docs retrieved   In a good system, precision decreases as either the number of docs retrieved or recall increases 0 matching results found.   People doing informa)on...
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This document was uploaded on 02/26/2014.

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