[ML][DOCS] Refer to rules feature as custom rules (#32785)
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[role="xpack"]
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[[ml-configuring-detector-custom-rules]]
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=== Customizing detectors with rules and filters
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=== Customizing detectors with custom rules
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<<ml-rules,Rules and filters>> enable you to change the behavior of anomaly
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<<ml-rules,Custom rules>> enable you to change the behavior of anomaly
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detectors based on domain-specific knowledge.
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Rules describe _when_ a detector should take a certain _action_ instead
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Custom rules describe _when_ a detector should take a certain _action_ instead
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of following its default behavior. To specify the _when_ a rule uses
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a `scope` and `conditions`. You can think of `scope` as the categorical
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specification of a rule, while `conditions` are the numerical part.
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@ -14,7 +14,7 @@ scope and conditions.
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Let us see how those can be configured by examples.
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==== Specifying rule scope
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==== Specifying custom rule scope
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Let us assume we are configuring a job in order to detect DNS data exfiltration.
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Our data contain fields "subdomain" and "highest_registered_domain".
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@ -127,7 +127,7 @@ PUT _xpack/ml/anomaly_detectors/scoping_multiple_fields
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Such a detector will skip results when the values of all 3 scoped fields
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are included in the referenced filters.
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==== Specifying rule conditions
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==== Specifying custom rule conditions
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Imagine a detector that looks for anomalies in CPU utilization.
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Given a machine that is idle for long enough, small movement in CPU could
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@ -206,9 +206,9 @@ PUT _xpack/ml/anomaly_detectors/rule_with_range
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----------------------------------
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// CONSOLE
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==== Rules in the life-cycle of a job
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==== Custom rules in the life-cycle of a job
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Rules only affect results created after the rules were applied.
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Custom rules only affect results created after the rules were applied.
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Let us imagine that we have configured a job and it has been running
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for some time. After observing its results we decide that we can employ
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rules in order to get rid of some uninteresting results. We can use
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@ -216,7 +216,7 @@ the {ref}/ml-update-job.html[update job API] to do so. However, the rule we
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added will only be in effect for any results created from the moment we added
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the rule onwards. Past results will remain unaffected.
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==== Using rules VS filtering data
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==== Using custom rules VS filtering data
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It might appear like using rules is just another way of filtering the data
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that feeds into a job. For example, a rule that skips results when the
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@ -13,4 +13,4 @@ A filter resource has the following properties:
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`items`::
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(array of strings) An array of strings which is the filter item list.
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For more information, see {stack-ov}/ml-rules.html[Machine learning rules and filters].
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For more information, see {stack-ov}/ml-rules.html[Machine learning custom rules].
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@ -18,7 +18,7 @@ Retrieves filters.
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===== Description
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You can get a single filter or all filters. For more information, see
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{stack-ov}/ml-rules.html[Machine learning rules and filters].
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{stack-ov}/ml-rules.html[Machine learning custom rules].
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==== Path Parameters
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@ -265,7 +265,7 @@ NOTE: The `field_name` cannot contain double quotes or backslashes.
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when there is no value for the by or partition fields. The default value is `false`.
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`custom_rules`::
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(array) An array of rule objects, which enable customizing how the detector works.
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(array) An array of custom rule objects, which enable customizing how the detector works.
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For example, a rule may dictate to the detector conditions under which results should be skipped.
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For more information see <<ml-detector-custom-rule,detector custom rule objects>>. +
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+
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@ -420,7 +420,7 @@ For more information, see
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{stack-ov}/ml-rules.html[Custom rules] enable you to customize the way detectors
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operate.
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A rule has the following properties:
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A custom rule has the following properties:
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`actions`::
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(array) The set of actions to be triggered when the rule applies.
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