63 lines
2.6 KiB
Plaintext
63 lines
2.6 KiB
Plaintext
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[role="xpack"]
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[[job-tips]]
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=== Machine Learning Job Tips
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++++
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<titleabbrev>Job Tips</titleabbrev>
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++++
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When you are creating a job in {kib}, the job creation wizards can provide
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advice based on the characteristics of your data. By heeding these suggestions,
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you can create jobs that are more likely to produce insightful {ml} results.
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[[bucket-span]]
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==== Bucket Span
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The bucket span is the time interval that {ml} analytics use to summarize and
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model data for your job. When you create a job in {kib}, you can choose to
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estimate a bucket span value based on your data characteristics. Typically, the
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estimated value is between 5 minutes to 1 hour. If you choose a value that is
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larger than one day or is significantly different than the estimated value, you
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receive an informational message. For more information about choosing an
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appropriate bucket span, see {xpack-ref}/ml-buckets.html[Buckets].
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[[cardinality]]
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==== Cardinality
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If there are logical groupings of related entities in your data, {ml} analytics
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can make data models and generate results that take these groupings into
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consideration. For example, you might choose to split your data by user ID and
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detect when users are accessing resources differently than they usually do.
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If the field that you use to split your data has many different values, the
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job uses more memory resources. In particular, if the cardinality of the
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`partition_field_name` is greater than 100, you are advised to consider
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alternative options such as population analysis.
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Likewise if you are performing population analysis and the cardinality of the
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`over_field_name` is below 10, you are advised that this might not be a suitable
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field to use.
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For more information, see
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{xpack-ref}/ml-configuring-pop.html[Performing Population Analysis].
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[[influencers]]
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==== Influencers
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When you create a job, you can specify _influencers_, which are also sometimes
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referred to as _key fields_. Picking an influencer is strongly recommended for
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the following reasons:
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* It allows you to more easily assign blame for the anomaly
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* It simplifies and aggregates the results
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The best influencer is the person or thing that you want to blame for the
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anomaly. In many cases, users or client IP addresses make excellent influencers.
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Influencers can be any field in your data; they do not need to be fields that
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are specified in your detectors, though they often are.
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As a best practice, do not pick too many influencers. For example, you generally
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do not need more than three. If you pick many influencers, the results can be
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overwhelming and there is a small overhead to the analysis.
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The job creation wizards in {kib} can suggest which fields to use as influencers.
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