Anomaly scores

Interpreting the results of Elastic ML's anomaly detection jobs first requires the ability to recognize the fact that there are several levels of scoring unusualness, expressed within the results. They are as follows:

• Bucket-level (result_type:bucket): This level summarizes the results of the entirety of the anomaly detection job per time bucket. Essentially, it is a representation of how unusual that time bucket is, given the configuration of your job.
• Influencer-level (result_type:influencer): This is used to better understand the most unusual entities (influencers) within a timespan.
• Record-level (result_type:record): This is the most detailed information regarding every anomalous occurrence or anomalous entity within a time bucket. Again, depending on the job configuration (multiple detectors, splits, and so on), there can be many record-level documents per time bucket.

Additionally, to fully appreciate how scoring is done, we also need...