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