In Bag of Tricks for Efficient Text Classification paper which is popular right now, he calculates prec@1 for the datasets in the experimentation segment. What does that mean?


Its "Precision at 1", or how often the highest ranked document is relevant:


Suppose you are looking for items about monkeys. Your query engine queries documents for "monkeys" and ranks by relevance. If the highest ranked document is indeed about monkeys, then that's a win for your query algorithm. But if the highest ranked document is ranked 1 because it has the text "Enough of your monkey business" then its a loss, because that's not really about monkeys.

Repeat over a bunch of search terms. The Precision-at-one is then the number of wins over the total number of search terms tried.

  • $\begingroup$ So essentially, in this paper (for sentiment analysis) it just means simple precision? $\endgroup$ Aug 10 '16 at 4:29
  • $\begingroup$ What's "simple" precision? $\endgroup$
    – Emre
    Aug 10 '16 at 16:10
  • $\begingroup$ Sorry for that. I mean prec@1 is essentially the precision i'll obtain if I use scikit's metrics library and calculate for multiclass classification? $\endgroup$ Aug 11 '16 at 9:03

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