Add a usage example of the JLH score (#28905)
Adds a usage example of the JLH score used in significant terms aggregation. All other methods to calculate significance score have such an example Closes #28513
This commit is contained in:
parent
e7d1e12675
commit
d018a0008e
|
@ -327,6 +327,15 @@ However, the `size` and `shard size` settings covered in the next section provid
|
||||||
==== Parameters
|
==== Parameters
|
||||||
|
|
||||||
===== JLH score
|
===== JLH score
|
||||||
|
The JLH score can be used as a significance score by adding the parameter
|
||||||
|
|
||||||
|
[source,js]
|
||||||
|
--------------------------------------------------
|
||||||
|
|
||||||
|
"jlh": {
|
||||||
|
}
|
||||||
|
--------------------------------------------------
|
||||||
|
// NOTCONSOLE
|
||||||
|
|
||||||
The scores are derived from the doc frequencies in _foreground_ and _background_ sets. The _absolute_ change in popularity (foregroundPercent - backgroundPercent) would favor common terms whereas the _relative_ change in popularity (foregroundPercent/ backgroundPercent) would favor rare terms. Rare vs common is essentially a precision vs recall balance and so the absolute and relative changes are multiplied to provide a sweet spot between precision and recall.
|
The scores are derived from the doc frequencies in _foreground_ and _background_ sets. The _absolute_ change in popularity (foregroundPercent - backgroundPercent) would favor common terms whereas the _relative_ change in popularity (foregroundPercent/ backgroundPercent) would favor rare terms. Rare vs common is essentially a precision vs recall balance and so the absolute and relative changes are multiplied to provide a sweet spot between precision and recall.
|
||||||
|
|
||||||
|
|
Loading…
Reference in New Issue