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[role="xpack"]
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[[ml-rules]]
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- === Machine learning custom rules
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- <titleabbrev>Custom Rules</titleabbrev>
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+ === Custom rules
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By default, as described in <<ml-analyzing>>, anomaly detection is unsupervised
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and the {ml} models have no awareness of the domain of your data. As a result,
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{ml} jobs might identify events that are statistically significant but are
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uninteresting when you know the larger context. Machine learning custom rules
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enable you to customize anomaly detection.
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- _Rules_ instruct anomaly detectors to change their behavior based on
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+ _Custom rules_ instruct anomaly detectors to change their behavior based on
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domain-specific knowledge that you provide. When you create a rule, you can
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specify conditions, scope, and actions. When the conditions of a rule are
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satisfied, its actions are triggered.
You can’t perform that action at this time.
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