p/r definitions
@@ -430,13 +430,11 @@ components are weighted equally.
<Accordion title="Understanding the training output and score types" spaced>
-<!-- TODO: come up with good short explanation of precision and recall -->
-
| Name | Description |
| -------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
| **Loss** | The training loss representing the amount of work left for the optimizer. Should decrease, but usually not to `0`. |
-| **Precision** (P) | Should increase. |
-| **Recall** (R) | Should increase. |
+| **Precision** (P) | The percentage of generated predictions that are correct. Should increase. |
+| **Recall** (R) | The percentage of gold-standard annotations that are in fact predicted. Should increase. |
| **F-Score** (F) | The weighted average of precision and recall. 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. |
