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Add precision/recall description

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Matthew Honnibal 2020-08-18 13:51:08 +02:00
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website/docs/usage/training.md
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@ -454,7 +454,22 @@ components are weighted equally.
| **UAS** / **LAS** | Unlabeled and labeled attachment score for the dependency parser, i.e. the percentage of correct arcs. Should increase. | | **UAS** / **LAS** | Unlabeled and labeled attachment score for the dependency parser, i.e. the percentage of correct arcs. Should increase. |
| **Words per second** (WPS) | Prediction speed in words per second. Should stay stable. | | **Words per second** (WPS) | Prediction speed in words per second. Should stay stable. |
Precision and recall are two common measurements of a model's accuracy. You
need precision and recall statistics whenever your model can return a variable
number of predictions, as in this situation there are two different ways your
model can be "accurate".
Precision refers to the percentage of predicted annotations that were correct,
while recall refers to the percentage of reference annotations recovered.
A model that only returns one entity for a document will have precision 1.0 if
that entity is correct, but might have low recall if it has missed lots of
other correct entities. F-score is the harmonic mean of precision and recall.
The harmonic mean is used instead of the arithmetic mean so that systems with
very low precision or very low recall will score lower than systems that
achieve a balance of the two.
<!-- TODO: is this still relevant? --> <!-- TODO: is this still relevant? -->
<!-- Yes (MH) -->
Note that if the development data has raw text, some of the gold-standard Note that if the development data has raw text, some of the gold-standard
entities might not align to the predicted tokenization. These tokenization entities might not align to the predicted tokenization. These tokenization