[[reference-ml-get_overall_buckets]]

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||        ██████╗ ███████╗ █████╗ ██████╗ ███╗   ███╗███████╗                                                            ||
||        ██╔══██╗██╔════╝██╔══██╗██╔══██╗████╗ ████║██╔════╝                                                            ||
||        ██████╔╝█████╗  ███████║██║  ██║██╔████╔██║█████╗                                                              ||
||        ██╔══██╗██╔══╝  ██╔══██║██║  ██║██║╚██╔╝██║██╔══╝                                                              ||
||        ██║  ██║███████╗██║  ██║██████╔╝██║ ╚═╝ ██║███████╗                                                            ||
||        ╚═╝  ╚═╝╚══════╝╚═╝  ╚═╝╚═════╝ ╚═╝     ╚═╝╚══════╝                                                            ||
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||    This file is autogenerated, DO NOT send pull requests that changes this file directly.                             ||
||    You should update the script that does the generation, which can be found in:                                      ||
||    https://github.com/elastic/elastic-client-generator-js                                                             ||
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||    You can run the script with the following command:                                                                 ||
||       npm run elasticsearch -- --version <version>                                                                    ||
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[discrete]
[[client.ml.getOverallBuckets]]
=== client.ml.getOverallBuckets

Get overall bucket results. Retrievs overall bucket results that summarize the bucket results of multiple anomaly detection jobs. The `overall_score` is calculated by combining the scores of all the buckets within the overall bucket span. First, the maximum `anomaly_score` per anomaly detection job in the overall bucket is calculated. Then the `top_n` of those scores are averaged to result in the `overall_score`. This means that you can fine-tune the `overall_score` so that it is more or less sensitive to the number of jobs that detect an anomaly at the same time. For example, if you set `top_n` to `1`, the `overall_score` is the maximum bucket score in the overall bucket. Alternatively, if you set `top_n` to the number of jobs, the `overall_score` is high only when all jobs detect anomalies in that overall bucket. If you set the `bucket_span` parameter (to a value greater than its default), the `overall_score` is the maximum `overall_score` of the overall buckets that have a span equal to the jobs' largest bucket span.

{ref}/ml-get-overall-buckets.html[{es} documentation]

[discrete]
==== Function signature

[source,ts]
----
(MlGetOverallBucketsRequest, options?): Promise<MlGetOverallBucketsResponse>
----

[discrete]
==== Request

[pass]
++++
<pre>
++++
interface MlGetOverallBucketsRequest extends <<RequestBase>> {
  job_id: <<Id>>
  allow_no_match?: boolean
  bucket_span?: <<Duration>>
  end?: <<DateTime>>
  exclude_interim?: boolean
  overall_score?: <<double>> | string
  start?: <<DateTime>>
  top_n?: <<integer>>
}

[pass]
++++
</pre>
++++
[discrete]
==== Response

[pass]
++++
<pre>
++++
interface MlGetOverallBucketsResponse {
  count: <<long>>
  overall_buckets: <<MlOverallBucket>>[]
}

[pass]
++++
</pre>
++++