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Another widely used metric for the effectiveness of IR models is the mean average precision (MAP).
The comparison showed a performance improvement regarding mean average precision (MAP) of up to 41%24%4% on average) when neither BugLocator nor BLUiR used bug similarity data.
We use Mean Average Precision (MAP), a popular rank evaluation method to evaluate the three models.
As most of the research works [2, 27 30], we use the mean average precision (MAP) to measure our performance.
Mean average precision (MAP): This metric considers the ranks of all the buggy files, not only the first one.
They found that, as the number of parties modelled increases the performance of recommendation decreases, in terms of the Mean Average Precision (MAP) [32] and F-measure [33].
Table 9 shows that, in general, usage of higher weights was able to increase AmaLgam's effectiveness, measured in terms of mean average precision (MAP).
Table 2 contains the mean average precision (MAP) and mean area under the receiver operating characteristics curve (MAROC) values for both the Soundwalks and Freesound databases.
In our GP framework, we have adopted the mean average precision (MAP) [2] measure to evaluate the quality of a ranking for a set of queries.
We evaluated this scenario by selecting the top 100 items with the highest predicted ratings for each user and applied the precision at n (prec@n) and the mean average precision (MAP) measures.
Another alternative metric is the mean average precision (MAP).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com