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[7.x][DOCS] Adds ML related items to release highlights (#55652)

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István Zoltán Szabó 2020-04-23 11:58:32 +02:00 committed by GitHub
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docs/reference/release-notes/highlights-7.7.0.asciidoc
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@ -26,12 +26,47 @@ https://issues.apache.org/jira/browse/LUCENE-9300[corresponding issue].
// tag::notable-highlights[]
[discrete]
=== {transforms-cap}
=== {transforms-cap} – now in GA!
We introduced {transforms} in 7.2.0 as a beta feature. It is now mature enough
to declare the feature GA (general availability). {transforms-cap} enable you to
pivot and summarize your data and store it in a new index. See
{ref}/transforms.html[{transforms-cap}] and
{ref}//transform-apis.html[{transform-cap} APIs].
In 7.7, we move {transforms} from beta to general availability.
{ref}/transforms.html[{transforms-cap}] enable you to pivot existing {es}
indices using group-by and aggregations into a destination feature index, which
provides opportunities for new insights and analytics. For example, you can use
{transforms} to pivot your data into entity-centric indices that summarize the
behavior of users or sessions or other entities in your data.
{transforms-cap} now include support for cross-cluster search. Allowing you to
create your destination feature index on a separate cluster from the source
indices.
Aggregation support has been expanded within {transforms} to include support for
{ref}/search-aggregations-metrics-percentile-aggregation.html[multi-value (percentiles)]
and
{ref}/search-aggregations-bucket-filter-aggregation.html[filter aggregations].
We also optimized the performance of the
{ref}/search-aggregations-bucket-datehistogram-aggregation.html[date histogram aggregations].
// end::notable-highlights[]
// tag::notable-highlights[]
[discrete]
=== Introducing multiclass {classification}
{ml-docs}/dfa-classification.html[{classification-cap}] using multiple classes
is now available in {dfanalytics}. {classification-cap} is a supervised {ml}
technique which has been already available as a binary process in the previous
release. Multiclass {classification} works well with up to 30 distinct
categories.
// end::notable-highlights[]
// tag::notable-highlights[]
[discrete]
=== {feat-imp-cap} at {infer} time
{feat-imp-cap} now can be calculated at {infer} time. This value provides
further insight into the results of a {classification} or {regression} job and
therefore helps interpret these results.
// end::notable-highlights[]