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The relation extraction algorithm, RelExOnt performs well with an average precision of 86.89%.
However, PMD has a higher average precision of 85% when compared to JSpIRIT (33%).
As can be seen, our detection algorithm yields an average precision of 97.1%.
The automatic detection yields an average precision of 59% at an average recall of 82% with high variation.
Laboratory testing indicated that the SBPVP has an average accuracy of ± 3% and an average precision of ± 2%.
If we consider our best performing model, we find that it has average precision of 0.3 and recall of 0.65.
We found that a single, parsimonious algorithm successfully interpreted numerous manifestations of loose speak with an average precision of 98% and an average recall of 90%.
This evaluation achieved an average precision of 89.23%, sensitivity of 88.83% and specificity of 94.92% when tested over 19 automotive drivers.
Overall, the fully automated algorithm achieves an average precision of 80.3% during behavioral experiments and 55.6% in trail videos, relative to the human-guided analysis process.
In the first setup, the box plot and Z-score had practically the same precision and K-means had an impressive result with an average precision of 99.89%.
JDeodorant has a better accuracy for Feature Envy when compared to both tools, with an average recall of 48% and an average precision of 13%.
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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