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[DOCS] Changes level offset in data frame analytics APIs (#59919) (#59926)
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docs/reference/ml/df-analytics/apis/delete-dfanalytics.asciidoc

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[role="xpack"]
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[testenv="platinum"]
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[[delete-dfanalytics]]
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=== Delete {dfanalytics-jobs} API
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= Delete {dfanalytics-jobs} API
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[subs="attributes"]
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++++
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<titleabbrev>Delete {dfanalytics-jobs}</titleabbrev>
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[[ml-delete-dfanalytics-request]]
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==== {api-request-title}
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== {api-request-title}
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`DELETE _ml/data_frame/analytics/<data_frame_analytics_id>`
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[[ml-delete-dfanalytics-prereq]]
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==== {api-prereq-title}
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== {api-prereq-title}
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If the {es} {security-features} are enabled, you must have the following built-in roles or equivalent privileges:
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[[ml-delete-dfanalytics-path-params]]
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==== {api-path-parms-title}
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== {api-path-parms-title}
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`<data_frame_analytics_id>`::
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(Required, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-data-frame-analytics]
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[[ml-delete-dfanalytics-query-params]]
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==== {api-query-parms-title}
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== {api-query-parms-title}
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`force`::
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(Optional, boolean) If `true`, it deletes a job that is not stopped; this method is
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[[ml-delete-dfanalytics-example]]
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==== {api-examples-title}
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== {api-examples-title}
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The following example deletes the `loganalytics` {dfanalytics-job}:
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docs/reference/ml/df-analytics/apis/delete-inference-trained-model.asciidoc

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[role="xpack"]
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[testenv="basic"]
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[[delete-inference]]
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=== Delete {infer} trained model API
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= Delete {infer} trained model API
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[subs="attributes"]
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<titleabbrev>Delete {infer} trained model</titleabbrev>
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[[ml-delete-inference-request]]
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==== {api-request-title}
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== {api-request-title}
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`DELETE _ml/inference/<model_id>`
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[[ml-delete-inference-prereq]]
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==== {api-prereq-title}
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== {api-prereq-title}
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If the {es} {security-features} are enabled, you must have the following built-in roles or equivalent privileges:
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[[ml-delete-inference-path-params]]
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==== {api-path-parms-title}
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== {api-path-parms-title}
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`<model_id>`::
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(Optional, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]
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[[ml-delete-inference-response-codes]]
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==== {api-response-codes-title}
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== {api-response-codes-title}
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`409`::
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The code indicates that the trained {infer} model is referenced by an ingest
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pipeline and cannot be deleted.
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[[ml-delete-inference-example]]
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==== {api-examples-title}
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== {api-examples-title}
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The following example deletes the `regression-job-one-1574775307356` trained
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model:

docs/reference/ml/df-analytics/apis/evaluate-dfanalytics.asciidoc

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[role="xpack"]
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[testenv="platinum"]
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[[evaluate-dfanalytics]]
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=== Evaluate {dfanalytics} API
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= Evaluate {dfanalytics} API
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[subs="attributes"]
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[[ml-evaluate-dfanalytics-request]]
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==== {api-request-title}
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== {api-request-title}
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`POST _ml/data_frame/_evaluate`
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[[ml-evaluate-dfanalytics-prereq]]
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==== {api-prereq-title}
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== {api-prereq-title}
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If the {es} {security-features} are enabled, you must have the following privileges:
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[[ml-evaluate-dfanalytics-desc]]
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==== {api-description-title}
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== {api-description-title}
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The API packages together commonly used evaluation metrics for various types of
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machine learning features. This has been designed for use on indexes created by
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[[ml-evaluate-dfanalytics-request-body]]
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==== {api-request-body-title}
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== {api-request-body-title}
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`evaluation`::
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(Required, object) Defines the type of evaluation you want to perform.
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source index. See <<query-dsl>>.
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[[ml-evaluate-dfanalytics-resources]]
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==== {dfanalytics-cap} evaluation resources
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== {dfanalytics-cap} evaluation resources
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[[binary-sc-resources]]
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===== Binary soft classification evaluation objects
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=== Binary soft classification evaluation objects
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Binary soft classification evaluates the results of an analysis which outputs
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the probability that each document belongs to a certain class. For example, in
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[[regression-evaluation-resources]]
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===== {regression-cap} evaluation objects
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=== {regression-cap} evaluation objects
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{regression-cap} evaluation evaluates the results of a {regression} analysis
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which outputs a prediction of values.
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[[classification-evaluation-resources]]
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==== {classification-cap} evaluation objects
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== {classification-cap} evaluation objects
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{classification-cap} evaluation evaluates the results of a {classanalysis} which
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outputs a prediction that identifies to which of the classes each document
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////
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[[ml-evaluate-dfanalytics-results]]
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==== {api-response-body-title}
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== {api-response-body-title}
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`binary_soft_classification`::
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(object) If you chose to do binary soft classification, the API returns the
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[[ml-evaluate-dfanalytics-example]]
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==== {api-examples-title}
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== {api-examples-title}
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[[ml-evaluate-binary-soft-class-example]]
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===== Binary soft classification
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=== Binary soft classification
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[source,console]
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--------------------------------------------------
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[[ml-evaluate-regression-example]]
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===== {regression-cap}
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=== {regression-cap}
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[source,console]
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--------------------------------------------------
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[[ml-evaluate-classification-example]]
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===== {classification-cap}
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=== {classification-cap}
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[source,console]

docs/reference/ml/df-analytics/apis/explain-dfanalytics.asciidoc

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[role="xpack"]
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[[explain-dfanalytics]]
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=== Explain {dfanalytics} API
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= Explain {dfanalytics} API
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[subs="attributes"]
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[[ml-explain-dfanalytics-request]]
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==== {api-request-title}
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== {api-request-title}
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`GET _ml/data_frame/analytics/_explain` +
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[[ml-explain-dfanalytics-prereq]]
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==== {api-prereq-title}
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== {api-prereq-title}
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If the {es} {security-features} are enabled, you must have the following privileges:
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[[ml-explain-dfanalytics-desc]]
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==== {api-description-title}
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== {api-description-title}
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This API provides explanations for a {dataframe-analytics-config} that either
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exists already or one that has not been created yet.
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[[ml-explain-dfanalytics-path-params]]
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==== {api-path-parms-title}
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== {api-path-parms-title}
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`<data_frame_analytics_id>`::
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(Optional, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-data-frame-analytics]
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[[ml-explain-dfanalytics-request-body]]
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==== {api-request-body-title}
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== {api-request-body-title}
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A {dataframe-analytics-config} as described in <<put-dfanalytics>>.
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Note that `id` and `dest` don't need to be provided in the context of this API.
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[role="child_attributes"]
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[[ml-explain-dfanalytics-results]]
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==== {api-response-body-title}
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== {api-response-body-title}
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The API returns a response that contains the following:
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[[ml-explain-dfanalytics-example]]
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==== {api-examples-title}
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== {api-examples-title}
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[source,console]
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--------------------------------------------------

docs/reference/ml/df-analytics/apis/get-dfanalytics-stats.asciidoc

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[[get-dfanalytics-stats]]
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=== Get {dfanalytics-jobs} statistics API
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= Get {dfanalytics-jobs} statistics API
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<titleabbrev>Get {dfanalytics-jobs} stats</titleabbrev>
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[[ml-get-dfanalytics-stats-request]]
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==== {api-request-title}
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== {api-request-title}
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`GET _ml/data_frame/analytics/<data_frame_analytics_id>/_stats` +
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[[ml-get-dfanalytics-stats-prereq]]
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==== {api-prereq-title}
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== {api-prereq-title}
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[[ml-get-dfanalytics-stats-path-params]]
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==== {api-path-parms-title}
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== {api-path-parms-title}
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`<data_frame_analytics_id>`::
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-data-frame-analytics-default]
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[[ml-get-dfanalytics-stats-query-params]]
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==== {api-query-parms-title}
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== {api-query-parms-title}
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`allow_no_match`::
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[[ml-get-dfanalytics-stats-response-body]]
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==== {api-response-body-title}
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`data_frame_analytics`::
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(array)
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[[ml-get-dfanalytics-stats-response-codes]]
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==== {api-response-codes-title}
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== {api-response-codes-title}
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`404` (Missing resources)::
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If `allow_no_match` is `false`, this code indicates that there are no
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[[ml-get-dfanalytics-stats-example]]
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==== {api-examples-title}
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== {api-examples-title}
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The following API retrieves usage information for the
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{ml-docs}/ecommerce-outliers.html[{oldetection} {dfanalytics-job} example]:

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