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[DOCS] Fine-tunes data frame analytics API docs formatting. (#50799)
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12 changed files with 28 additions and 19 deletions
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@ -21,7 +21,8 @@ experimental[]
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[[ml-delete-dfanalytics-prereq]]
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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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If the {es} {security-features} are enabled, you must have the following
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built-in roles or equivalent privileges:
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* `machine_learning_admin`
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* `kibana_user` (UI only)
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@ -22,7 +22,8 @@ experimental[]
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[[ml-delete-inference-prereq]]
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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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If the {es} {security-features} are enabled, you must have the following
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built-in roles or equivalent privileges:
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* `machine_learning_admin`
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* `kibana_user` (UI only)
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@ -22,7 +22,8 @@ experimental[]
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[[ml-evaluate-dfanalytics-prereq]]
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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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If the {es} {security-features} are enabled, you must have the following
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privileges:
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* cluster: `monitor_ml`
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@ -28,7 +28,8 @@ experimental[]
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[[ml-explain-dfanalytics-prereq]]
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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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If the {es} {security-features} are enabled, you must have the following
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privileges:
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* cluster: `monitor_ml`
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@ -28,7 +28,8 @@ experimental[]
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[[ml-get-dfanalytics-stats-prereq]]
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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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If the {es} {security-features} are enabled, you must have the following
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privileges:
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* cluster: `monitor_ml`
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@ -27,7 +27,8 @@ experimental[]
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[[ml-get-dfanalytics-prereq]]
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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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If the {es} {security-features} are enabled, you must have the following
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privileges:
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* cluster: `monitor_ml`
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@ -29,7 +29,8 @@ experimental[]
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[[ml-get-inference-stats-prereq]]
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==== {api-prereq-title}
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Required privileges which should be added to a custom role:
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If the {es} {security-features} are enabled, you must have the following
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privileges:
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* cluster: `monitor_ml`
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@ -29,7 +29,8 @@ experimental[]
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[[ml-get-inference-prereq]]
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==== {api-prereq-title}
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Required privileges which should be added to a custom role:
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If the {es} {security-features} are enabled, you must have the following
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privileges:
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* cluster: `monitor_ml`
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@ -20,7 +20,8 @@ experimental[]
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[[ml-put-dfanalytics-prereq]]
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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 and privileges:
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If the {es} {security-features} are enabled, you must have the following
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built-in roles and privileges:
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* `machine_learning_admin`
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* `kibana_user` (UI only)
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@ -66,12 +67,11 @@ upper bound on the improvement in validation loss.
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A fixed number of rounds is used for optimization which depends on the number of
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parameters being optimized. The optimization starts with random search, then
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Bayesian optimization is performed that is targeting maximum expected
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improvement. If you override any parameters,
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//TBD: What is meant by overriding them? Explicitly setting the parameter instead of letting it take the default?
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the optimization calculates the value of the remaining parameters accordingly
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and uses the value you provided for the overridden parameter. The number of
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rounds are reduced respectively. The validation error is estimated in each round
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by using 4-fold cross validation.
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improvement. If you override any parameters by explicitely setting it, the
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optimization calculates the value of the remaining parameters accordingly and
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uses the value you provided for the overridden parameter. The number of rounds
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are reduced respectively. The validation error is estimated in each round by
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using 4-fold cross validation.
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[[ml-put-dfanalytics-path-params]]
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==== {api-path-parms-title}
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@ -104,7 +104,6 @@ TIP: Advanced parameters are for fine-tuning {classanalysis}. They are set
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automatically by <<ml-hyperparam-optimization,hyperparameter optimization>>
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to give minimum validation error. It is highly recommended to use the default
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values unless you fully understand the function of these parameters.
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--
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`analysis`.`classification`.`dependent_variable`::::
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@ -20,7 +20,8 @@ experimental[]
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[[ml-start-dfanalytics-prereq]]
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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 and privileges:
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If the {es} {security-features} are enabled, you must have the following
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built-in roles and privileges:
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* `machine_learning_admin`
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* `kibana_user` (UI only)
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@ -24,7 +24,8 @@ experimental[]
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[[ml-stop-dfanalytics-prereq]]
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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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If the {es} {security-features} are enabled, you must have the following
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built-in roles or equivalent privileges:
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* `machine_learning_admin`
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* `kibana_user` (UI only)
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@ -3,7 +3,7 @@
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== Definitions
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The role mappings resource definition you can find below is used in APIs related
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to security features.
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to {security-features}.
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* <<role-mapping-resources,Role mappings>>
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