At a lower level of abstraction, there are results at the record level. Giving the most amount of detail, record results show specific instances of anomalies and essentially answers the question "what entity was unusual and by how much?" To understand the structure and content of record-level results, let's query the results for a particular ML job. We will start by looking at the following results, which are for a simple, single metric job that has no defined influencers:
GET .ml-anomalies-*/_search{ "query": { "bool": { "filter": [ { "range" : { "timestamp" : { "gte": "now-2y" } } }, { "term" : { "job_id" : "farequote_single" } }, { "term" : { "result_type" : "record" } }, { "range" : { "record_score" : {"gte" : "90"}}}
