[role="xpack"] [[ml-configuring-pop]] === Performing population analysis Entities or events in your data can be considered anomalous when: * Their behavior changes over time, relative to their own previous behavior, or * Their behavior is different than other entities in a specified population. The latter method of detecting outliers is known as _population analysis_. The {ml} analytics build a profile of what a "typical" user, machine, or other entity does over a specified time period and then identify when one is behaving abnormally compared to the population. This type of analysis is most useful when the behavior of the population as a whole is mostly homogeneous and you want to identify outliers. In general, population analysis is not useful when members of the population inherently have vastly different behavior. You can, however, segment your data into groups that behave similarly and run these as separate jobs. For example, you can use a query filter in the {dfeed} to segment your data or you can use the `partition_field_name` to split the analysis for the different groups. Population analysis scales well and has a lower resource footprint than individual analysis of each series. For example, you can analyze populations of hundreds of thousands or millions of entities. To specify the population, use the `over_field_name` property. For example: [source,js] ---------------------------------- PUT _xpack/ml/anomaly_detectors/population { "description" : "Population analysis", "analysis_config" : { "bucket_span":"10m", "influencers": [ "username" ], "detectors": [ { "function": "mean", "field_name": "bytesSent", "over_field_name": "username" <1> } ] }, "data_description" : { "time_field":"@timestamp", "time_format": "epoch_ms" } } ---------------------------------- //CONSOLE <1> This `over_field_name` property indicates that the metrics for each user ( as identified by their `username` value) are analyzed relative to other users in each bucket. //TO-DO: Per sophiec20 "Perhaps add the datafeed config and add a query filter to //include only workstations as servers and printers would behave differently //from the population If your data is stored in {es}, you can use the population job wizard in {kib} to create a job with these same properties. For example, the population job wizard provides the following job settings: [role="screenshot"] image::images/ml-population-job.jpg["Job settings in the population job wizard] After you open the job and start the {dfeed} or supply data to the job, you can view the results in {kib}. For example, you can view the results in the **Anomaly Explorer**: [role="screenshot"] image::images/ml-population-results.jpg["Population analysis results in the Anomaly Explorer"] As in this case, the results are often quite sparse. There might be just a few data points for the selected time period. Population analysis is particularly useful when you have many entities and the data for specific entitles is sporadic or sparse. If you click on a section in the timeline or swimlanes, you can see more details about the anomalies: [role="screenshot"] image::images/ml-population-anomaly.jpg["Anomaly details for a specific user"] In this example, the user identified as `antonette` sent a high volume of bytes on the date and time shown. This event is anomalous because the mean is two times higher than the expected behavior of the population.