[DOCS] Fine-tunes data frame analytics API docs formatting. (#50799)

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István Zoltán Szabó 2020-01-09 16:21:01 +01:00 committed by GitHub
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12 changed files with 28 additions and 19 deletions

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@ -21,7 +21,8 @@ experimental[]
[[ml-delete-dfanalytics-prereq]]
==== {api-prereq-title}
If the {es} {security-features} are enabled, you must have the following built-in roles or equivalent privileges:
If the {es} {security-features} are enabled, you must have the following
built-in roles or equivalent privileges:
* `machine_learning_admin`
* `kibana_user` (UI only)

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@ -22,7 +22,8 @@ experimental[]
[[ml-delete-inference-prereq]]
==== {api-prereq-title}
If the {es} {security-features} are enabled, you must have the following built-in roles or equivalent privileges:
If the {es} {security-features} are enabled, you must have the following
built-in roles or equivalent privileges:
* `machine_learning_admin`
* `kibana_user` (UI only)

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@ -22,7 +22,8 @@ experimental[]
[[ml-evaluate-dfanalytics-prereq]]
==== {api-prereq-title}
If the {es} {security-features} are enabled, you must have the following privileges:
If the {es} {security-features} are enabled, you must have the following
privileges:
* cluster: `monitor_ml`

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@ -28,7 +28,8 @@ experimental[]
[[ml-explain-dfanalytics-prereq]]
==== {api-prereq-title}
If the {es} {security-features} are enabled, you must have the following privileges:
If the {es} {security-features} are enabled, you must have the following
privileges:
* cluster: `monitor_ml`

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@ -28,7 +28,8 @@ experimental[]
[[ml-get-dfanalytics-stats-prereq]]
==== {api-prereq-title}
If the {es} {security-features} are enabled, you must have the following privileges:
If the {es} {security-features} are enabled, you must have the following
privileges:
* cluster: `monitor_ml`

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@ -27,7 +27,8 @@ experimental[]
[[ml-get-dfanalytics-prereq]]
==== {api-prereq-title}
If the {es} {security-features} are enabled, you must have the following privileges:
If the {es} {security-features} are enabled, you must have the following
privileges:
* cluster: `monitor_ml`

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@ -29,7 +29,8 @@ experimental[]
[[ml-get-inference-stats-prereq]]
==== {api-prereq-title}
Required privileges which should be added to a custom role:
If the {es} {security-features} are enabled, you must have the following
privileges:
* cluster: `monitor_ml`

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@ -29,7 +29,8 @@ experimental[]
[[ml-get-inference-prereq]]
==== {api-prereq-title}
Required privileges which should be added to a custom role:
If the {es} {security-features} are enabled, you must have the following
privileges:
* cluster: `monitor_ml`

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@ -20,7 +20,8 @@ experimental[]
[[ml-put-dfanalytics-prereq]]
==== {api-prereq-title}
If the {es} {security-features} are enabled, you must have the following built-in roles and privileges:
If the {es} {security-features} are enabled, you must have the following
built-in roles and privileges:
* `machine_learning_admin`
* `kibana_user` (UI only)
@ -66,12 +67,11 @@ upper bound on the improvement in validation loss.
A fixed number of rounds is used for optimization which depends on the number of
parameters being optimized. The optimization starts with random search, then
Bayesian optimization is performed that is targeting maximum expected
improvement. If you override any parameters,
//TBD: What is meant by overriding them? Explicitly setting the parameter instead of letting it take the default?
the optimization calculates the value of the remaining parameters accordingly
and uses the value you provided for the overridden parameter. The number of
rounds are reduced respectively. The validation error is estimated in each round
by using 4-fold cross validation.
improvement. If you override any parameters by explicitely setting it, the
optimization calculates the value of the remaining parameters accordingly and
uses the value you provided for the overridden parameter. The number of rounds
are reduced respectively. The validation error is estimated in each round by
using 4-fold cross validation.
[[ml-put-dfanalytics-path-params]]
==== {api-path-parms-title}
@ -104,7 +104,6 @@ TIP: Advanced parameters are for fine-tuning {classanalysis}. They are set
automatically by <<ml-hyperparam-optimization,hyperparameter optimization>>
to give minimum validation error. It is highly recommended to use the default
values unless you fully understand the function of these parameters.
--
`analysis`.`classification`.`dependent_variable`::::

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@ -20,7 +20,8 @@ experimental[]
[[ml-start-dfanalytics-prereq]]
==== {api-prereq-title}
If the {es} {security-features} are enabled, you must have the following built-in roles and privileges:
If the {es} {security-features} are enabled, you must have the following
built-in roles and privileges:
* `machine_learning_admin`
* `kibana_user` (UI only)

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@ -24,7 +24,8 @@ experimental[]
[[ml-stop-dfanalytics-prereq]]
==== {api-prereq-title}
If the {es} {security-features} are enabled, you must have the following built-in roles or equivalent privileges:
If the {es} {security-features} are enabled, you must have the following
built-in roles or equivalent privileges:
* `machine_learning_admin`
* `kibana_user` (UI only)

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@ -3,7 +3,7 @@
== Definitions
The role mappings resource definition you can find below is used in APIs related
to security features.
to {security-features}.
* <<role-mapping-resources,Role mappings>>