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@@ -1,3 +1,306 @@
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+tag::allow-lazy-open[]
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+Advanced configuration option. Specifies whether this job can open when there is
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+insufficient {ml} node capacity for it to be immediately assigned to a node. The
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+default value is `false`; if a {ml} node with capacity to run the job cannot immediately be found, the <<ml-open-job,open {anomaly-jobs} API>> returns an
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+error. However, this is also subject to the cluster-wide
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+`xpack.ml.max_lazy_ml_nodes` setting; see <<advanced-ml-settings>>. If this
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+option is set to `true`, the <<ml-open-job,open {anomaly-jobs} API>> does not
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+return an error and the job waits in the `opening` state until sufficient {ml}
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+node capacity is available.
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+end::allow-lazy-open[]
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+
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+tag::allow-no-jobs[]
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+Specifies what to do when the request:
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++
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+--
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+* Contains wildcard expressions and there are no jobs that match.
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+* Contains the `_all` string or no identifiers and there are no matches.
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+* Contains wildcard expressions and there are only partial matches.
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+
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+The default value is `true`, which returns an empty `jobs` array
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+when there are no matches and the subset of results when there are partial
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+matches. If this parameter is `false`, the request returns a `404` status code
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+when there are no matches or only partial matches.
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+--
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+end::allow-no-jobs[]
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+
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+tag::allow-no-match[]
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+ Specifies what to do when the request:
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++
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+--
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+* Contains wildcard expressions and there are no {dfanalytics-jobs} that match.
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+* Contains the `_all` string or no identifiers and there are no matches.
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+* Contains wildcard expressions and there are only partial matches.
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+
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+The default value is `true`, which returns an empty `data_frame_analytics` array
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+when there are no matches and the subset of results when there are partial
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+matches. If this parameter is `false`, the request returns a `404` status code
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+when there are no matches or only partial matches.
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+--
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+end::allow-no-match[]
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+
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+tag::analysis-config[]
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+The analysis configuration, which specifies how to analyze the data.
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+After you create a job, you cannot change the analysis configuration; all
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+the properties are informational. An analysis configuration object has the following properties:
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+
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+`bucket_span`:::
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+(<<time-units,time units>>)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-span]
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+
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+`categorization_field_name`:::
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+(string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=categorization-field-name]
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+
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+`categorization_filters`:::
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+(array of strings)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=categorization-filters]
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+
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+`categorization_analyzer`:::
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+(object or string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=categorization-analyzer]
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+
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+`detectors`:::
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+(array) An array of detector configuration objects. Detector configuration
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+objects specify which data fields a job analyzes. They also specify which
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+analytical functions are used. You can specify multiple detectors for a job.
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+include::{docdir}/ml/ml-shared.asciidoc[tag=detector]
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++
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+--
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+NOTE: If the `detectors` array does not contain at least one detector,
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+no analysis can occur and an error is returned.
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+
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+--
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+
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+`influencers`:::
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+(array of strings)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=influencers]
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+
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+`latency`:::
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+(time units)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=latency]
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+
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+`multivariate_by_fields`:::
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+(boolean)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=multivariate-by-fields]
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+
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+`summary_count_field_name`:::
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+(string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=summary-count-field-name]
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+
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+end::analysis-config[]
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+
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+tag::analysis-limits[]
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+Limits can be applied for the resources required to hold the mathematical models
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+in memory. These limits are approximate and can be set per job. They do not
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+control the memory used by other processes, for example the {es} Java
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+processes. If necessary, you can increase the limits after the job is created.
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+The `analysis_limits` object has the following properties:
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+
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+`categorization_examples_limit`:::
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+(long)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=categorization-examples-limit]
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+
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+`model_memory_limit`:::
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+(long or string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=model-memory-limit]
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+end::analysis-limits[]
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+
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+tag::background-persist-interval[]
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+Advanced configuration option. The time between each periodic persistence of the
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+model. The default value is a randomized value between 3 to 4 hours, which
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+avoids all jobs persisting at exactly the same time. The smallest allowed value
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+is 1 hour.
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++
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+--
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+TIP: For very large models (several GB), persistence could take 10-20 minutes,
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+so do not set the `background_persist_interval` value too low.
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+
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+--
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+end::background-persist-interval[]
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+
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+tag::bucket-span[]
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+The size of the interval that the analysis is aggregated into, typically between
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+`5m` and `1h`. The default value is `5m`. For more information about time units,
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+see <<time-units>>.
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+end::bucket-span[]
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+
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+tag::by-field-name[]
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+The field used to split the data. In particular, this property is used for
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+analyzing the splits with respect to their own history. It is used for finding
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+unusual values in the context of the split.
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+end::by-field-name[]
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+
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+tag::categorization-analyzer[]
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+If `categorization_field_name` is specified, you can also define the analyzer
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+that is used to interpret the categorization field. This property cannot be used
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+at the same time as `categorization_filters`. The categorization analyzer
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+specifies how the `categorization_field` is interpreted by the categorization
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+process. The syntax is very similar to that used to define the `analyzer` in the
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+<<indices-analyze,Analyze endpoint>>. For more information, see
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+{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
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++
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+--
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+The `categorization_analyzer` field can be specified either as a string or as an
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+object. If it is a string it must refer to a
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+<<analysis-analyzers,built-in analyzer>> or one added by another plugin. If it
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+is an object it has the following properties:
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+--
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+
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+`char_filter`::::
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+(array of strings or objects)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=char-filter]
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+
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+`tokenizer`::::
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+(string or object)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=tokenizer]
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+
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+`filter`::::
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+(array of strings or objects)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=filter]
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+end::categorization-analyzer[]
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+
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+tag::categorization-examples-limit[]
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+The maximum number of examples stored per category in memory and in the results
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+data store. The default value is 4. If you increase this value, more examples
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+are available, however it requires that you have more storage available. If you
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+set this value to `0`, no examples are stored.
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++
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+--
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+NOTE: The `categorization_examples_limit` only applies to analysis that uses
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+categorization. For more information, see
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+{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
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+
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+--
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+end::categorization-examples-limit[]
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+
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+tag::categorization-field-name[]
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+If this property is specified, the values of the specified field will be
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+categorized. The resulting categories must be used in a detector by setting
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+`by_field_name`, `over_field_name`, or `partition_field_name` to the keyword
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+`mlcategory`. For more information, see
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+{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
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+end::categorization-field-name[]
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+
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+tag::categorization-filters[]
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+If `categorization_field_name` is specified, you can also define optional
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+filters. This property expects an array of regular expressions. The expressions
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+are used to filter out matching sequences from the categorization field values.
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+You can use this functionality to fine tune the categorization by excluding sequences from consideration when categories are defined. For example, you can exclude SQL statements that appear in your log files. For more information, see
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+{stack-ov}/ml-configuring-categories.html[Categorizing log messages]. This
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+property cannot be used at the same time as `categorization_analyzer`. If you
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+only want to define simple regular expression filters that are applied prior to
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+tokenization, setting this property is the easiest method. If you also want to
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+customize the tokenizer or post-tokenization filtering, use the
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+`categorization_analyzer` property instead and include the filters as
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+`pattern_replace` character filters. The effect is exactly the same.
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+end::categorization-filters[]
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+
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+tag::char-filter[]
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+One or more <<analysis-charfilters,character filters>>. In addition to the
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+built-in character filters, other plugins can provide more character filters.
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+This property is optional. If it is not specified, no character filters are
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+applied prior to categorization. If you are customizing some other aspect of the
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+analyzer and you need to achieve the equivalent of `categorization_filters`
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+(which are not permitted when some other aspect of the analyzer is customized),
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+add them here as
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+<<analysis-pattern-replace-charfilter,pattern replace character filters>>.
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+end::char-filter[]
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+
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+tag::custom-rules[]
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+An array of custom rule objects, which enable you to customize the way detectors
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+operate. For example, a rule may dictate to the detector conditions under which
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+results should be skipped. For more examples, see
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+{stack-ov}/ml-configuring-detector-custom-rules.html[Configuring detector custom rules].
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+A custom rule has the following properties:
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++
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+--
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+`actions`::
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+(array) The set of actions to be triggered when the rule applies. If
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+more than one action is specified the effects of all actions are combined. The
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+available actions include:
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+
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+* `skip_result`: The result will not be created. This is the default value.
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+Unless you also specify `skip_model_update`, the model will be updated as usual
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+with the corresponding series value.
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+* `skip_model_update`: The value for that series will not be used to update the
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+model. Unless you also specify `skip_result`, the results will be created as
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+usual. This action is suitable when certain values are expected to be
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+consistently anomalous and they affect the model in a way that negatively
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+impacts the rest of the results.
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+
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+`scope`::
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+(object) An optional scope of series where the rule applies. A rule must either
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+have a non-empty scope or at least one condition. By default, the scope includes
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+all series. Scoping is allowed for any of the fields that are also specified in
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+`by_field_name`, `over_field_name`, or `partition_field_name`. To add a scope
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+for a field, add the field name as a key in the scope object and set its value
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+to an object with the following properties:
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+
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+`filter_id`:::
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+(string) The id of the filter to be used.
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+
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+`filter_type`:::
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+(string) Either `include` (the rule applies for values in the filter) or
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+`exclude` (the rule applies for values not in the filter). Defaults to `include`.
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+
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+`conditions`::
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+(array) An optional array of numeric conditions when the rule applies. A rule
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+must either have a non-empty scope or at least one condition. Multiple
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+conditions are combined together with a logical `AND`. A condition has the
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+following properties:
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+
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+`applies_to`:::
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+(string) Specifies the result property to which the condition applies. The
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+available options are `actual`, `typical`, `diff_from_typical`, `time`.
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+
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+`operator`:::
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+(string) Specifies the condition operator. The available options are `gt`
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+(greater than), `gte` (greater than or equals), `lt` (less than) and `lte` (less
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+than or equals).
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+
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+`value`:::
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+(double) The value that is compared against the `applies_to` field using the
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+`operator`.
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+--
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++
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+--
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+NOTE: If your detector uses `lat_long`, `metric`, `rare`, or `freq_rare`
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+functions, you can only specify `conditions` that apply to `time`.
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+
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+--
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+end::custom-rules[]
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+
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+tag::custom-settings[]
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+Advanced configuration option. Contains custom meta data about the job. For
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+example, it can contain custom URL information as shown in
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+{stack-ov}/ml-configuring-url.html[Adding custom URLs to {ml} results].
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+end::custom-settings[]
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+
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+tag::data-description[]
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+The data description defines the format of the input data when you send data to
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+the job by using the <<ml-post-data,post data>> API. Note that when configure
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+a {dfeed}, these properties are automatically set.
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++
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+--
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+When data is received via the <<ml-post-data,post data>> API, it is not stored
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+in {es}. Only the results for {anomaly-detect} are retained.
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+
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+A data description object has the following properties:
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+
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+`format`:::
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+ (string) Only `JSON` format is supported at this time.
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+
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+`time_field`:::
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+ (string) The name of the field that contains the timestamp.
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+ The default value is `time`.
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+
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+`time_format`:::
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+(string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=time-format]
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+--
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+end::data-description[]
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+
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tag::dependent_variable[]
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`dependent_variable`::
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(Required, string) Defines which field of the document is to be predicted.
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@@ -7,6 +310,70 @@ that document will not be used for training, but a prediction with the trained
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model will be generated for it. It is also known as continuous target variable.
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end::dependent_variable[]
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+tag::detector-description[]
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+A description of the detector. For example, `Low event rate`.
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+end::detector-description[]
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+
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+tag::detector-field-name[]
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+The field that the detector uses in the function. If you use an event rate
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+function such as `count` or `rare`, do not specify this field.
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++
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+--
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+NOTE: The `field_name` cannot contain double quotes or backslashes.
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+
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+--
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+end::detector-field-name[]
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+
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+tag::detector-index[]
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+A unique identifier for the detector. This identifier is based on the order of
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+the detectors in the `analysis_config`, starting at zero. You can use this
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+identifier when you want to update a specific detector.
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+end::detector-index[]
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+
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+tag::detector[]
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+A detector has the following properties:
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+
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+`by_field_name`::::
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+(string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=by-field-name]
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+
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+`custom_rules`::::
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+(array)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=custom-rules]
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+
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+`detector_description`::::
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+(string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=detector-description]
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+
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+`detector_index`::::
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+(integer)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=detector-index]
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+
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+`exclude_frequent`::::
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+(string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=exclude-frequent]
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+
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+`field_name`::::
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+(string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=detector-field-name]
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+
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+`function`::::
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+(string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=function]
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+
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+`over_field_name`::::
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+(string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=over-field-name]
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+
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+`partition_field_name`::::
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+(string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=partition-field-name]
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+
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+`use_null`::::
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+(boolean)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=use-null]
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+
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+end::detector[]
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tag::eta[]
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`eta`::
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@@ -17,6 +384,13 @@ https://en.wikipedia.org/wiki/Gradient_boosting#Shrinkage[this wiki article]
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about shrinkage.
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end::eta[]
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+tag::exclude-frequent[]
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+Contains one of the following values: `all`, `none`, `by`, or `over`. If set,
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+frequent entities are excluded from influencing the anomaly results. Entities
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+can be considered frequent over time or frequent in a population. If you are
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+working with both over and by fields, then you can set `exclude_frequent` to
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+`all` for both fields, or to `by` or `over` for those specific fields.
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+end::exclude-frequent[]
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tag::feature_bag_fraction[]
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`feature_bag_fraction`::
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@@ -24,6 +398,18 @@ tag::feature_bag_fraction[]
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selecting a random bag for each candidate split.
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end::feature_bag_fraction[]
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+tag::filter[]
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+One or more <<analysis-tokenfilters,token filters>>. In addition to the built-in
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+token filters, other plugins can provide more token filters. This property is
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+optional. If it is not specified, no token filters are applied prior to
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+categorization.
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+end::filter[]
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+
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+tag::function[]
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+The analysis function that is used. For example, `count`, `rare`, `mean`, `min`,
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+`max`, and `sum`. For more information, see
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+{stack-ov}/ml-functions.html[Function reference].
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+end::function[]
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tag::gamma[]
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`gamma`::
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@@ -34,8 +420,67 @@ prefer smaller trees. The smaller this parameter the larger individual trees
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will be and the longer train will take.
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end::gamma[]
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+tag::groups[]
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+A list of job groups. A job can belong to no groups or many.
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+end::groups[]
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+
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+tag::influencers[]
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+A comma separated list of influencer field names. Typically these can be the by,
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+over, or partition fields that are used in the detector configuration. You might
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+also want to use a field name that is not specifically named in a detector, but
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+is available as part of the input data. When you use multiple detectors, the use
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+of influencers is recommended as it aggregates results for each influencer entity.
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+end::influencers[]
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+
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+tag::job-id-anomaly-detection[]
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+Identifier for the {anomaly-job}.
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+end::job-id-anomaly-detection[]
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+
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+tag::job-id-data-frame-analytics[]
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+Identifier for the {dfanalytics-job}.
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+end::job-id-data-frame-analytics[]
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+
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+tag::job-id-anomaly-detection-default[]
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+Identifier for the {anomaly-job}. It can be a job identifier, a group name, or a wildcard expression. If you do not specify one of these options, the API returns information for all {anomaly-jobs}.
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+end::job-id-anomaly-detection-default[]
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+
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+tag::job-id-data-frame-analytics-default[]
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+Identifier for the {dfanalytics-job}. If you do not specify this option, the API
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+returns information for the first hundred {dfanalytics-jobs}.
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+end::job-id-data-frame-analytics-default[]
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+
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+tag::job-id-anomaly-detection-list[]
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+An identifier for the {anomaly-jobs}. It can be a job
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+identifier, a group name, or a comma-separated list of jobs or groups.
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+end::job-id-anomaly-detection-list[]
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+
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+tag::job-id-anomaly-detection-wildcard[]
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+Identifier for the {anomaly-job}. It can be a job identifier, a group name, or a wildcard expression.
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+end::job-id-anomaly-detection-wildcard[]
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+
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+tag::job-id-anomaly-detection-wildcard-list[]
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+Identifier for the {anomaly-job}. It can be a job identifier, a group name, a
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+comma-separated list of jobs or groups, or a wildcard expression.
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+end::job-id-anomaly-detection-wildcard-list[]
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+
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+tag::job-id-anomaly-detection-define[]
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+Identifier for the {anomaly-job}. This identifier can contain lowercase alphanumeric
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+characters (a-z and 0-9), hyphens, and underscores. It must start and end with
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+alphanumeric characters.
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+end::job-id-anomaly-detection-define[]
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+
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+tag::job-id-data-frame-analytics-define[]
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+Identifier for the {dfanalytics-job}. This identifier can contain lowercase
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+alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start
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+and end with alphanumeric characters.
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+end::job-id-data-frame-analytics-define[]
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-tag::lambda[]
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+tag::jobs-stats-anomaly-detection[]
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+An array of {anomaly-job} statistics objects.
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+For more information, see <<ml-jobstats>>.
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+end::jobs-stats-anomaly-detection[]
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+
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+tag::lambda[]
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`lambda`::
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(Optional, double) Regularization parameter to prevent overfitting on the
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training dataset. Multiplies an L2 regularisation term which applies to leaf
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@@ -46,6 +491,16 @@ relevant relationships between the features and the {depvar}. The smaller this
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parameter the larger individual trees will be and the longer train will take.
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end::lambda[]
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+tag::latency[]
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+The size of the window in which to expect data that is out of time order. The
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+default value is 0 (no latency). If you specify a non-zero value, it must be greater than or equal to one second. For more information about time units, see <<time-units>>.
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++
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+--
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+NOTE: Latency is only applicable when you send data by using
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+the <<ml-post-data,post data>> API.
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+
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+--
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+end::latency[]
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tag::maximum_number_trees[]
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`maximum_number_trees`::
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@@ -53,6 +508,106 @@ tag::maximum_number_trees[]
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to contain. The maximum value is 2000.
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end::maximum_number_trees[]
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+tag::model-memory-limit[]
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+The approximate maximum amount of memory resources that are required for
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+analytical processing. Once this limit is approached, data pruning becomes
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+more aggressive. Upon exceeding this limit, new entities are not modeled. The
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+default value for jobs created in version 6.1 and later is `1024mb`.
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+This value will need to be increased for jobs that are expected to analyze high
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+cardinality fields, but the default is set to a relatively small size to ensure
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+that high resource usage is a conscious decision. The default value for jobs
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+created in versions earlier than 6.1 is `4096mb`.
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++
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+--
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+If you specify a number instead of a string, the units are assumed to be MiB.
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+Specifying a string is recommended for clarity. If you specify a byte size unit
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+of `b` or `kb` and the number does not equate to a discrete number of megabytes,
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+it is rounded down to the closest MiB. The minimum valid value is 1 MiB. If you
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+specify a value less than 1 MiB, an error occurs. For more information about
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+supported byte size units, see <<byte-units>>.
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+
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+If your `elasticsearch.yml` file contains an `xpack.ml.max_model_memory_limit`
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+setting, an error occurs when you try to create jobs that have
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+`model_memory_limit` values greater than that setting. For more information,
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+see <<ml-settings>>.
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+--
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+end::model-memory-limit[]
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+
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+tag::model-plot-config[]
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+This advanced configuration option stores model information along with the
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+results. It provides a more detailed view into {anomaly-detect}.
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++
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+--
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+WARNING: If you enable model plot it can add considerable overhead to the performance
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+of the system; it is not feasible for jobs with many entities.
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+
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+Model plot provides a simplified and indicative view of the model and its bounds.
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+It does not display complex features such as multivariate correlations or multimodal data.
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+As such, anomalies may occasionally be reported which cannot be seen in the model plot.
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+
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+Model plot config can be configured when the job is created or updated later. It must be
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+disabled if performance issues are experienced.
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+
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+The `model_plot_config` object has the following properties:
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+
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+`enabled`:::
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+(boolean) If true, enables calculation and storage of the model bounds for
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+each entity that is being analyzed. By default, this is not enabled.
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+
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+`terms`:::
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+experimental[] (string) Limits data collection to this comma separated list of
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+partition or by field values. If terms are not specified or it is an empty
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+string, no filtering is applied. For example, "CPU,NetworkIn,DiskWrites".
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+Wildcards are not supported. Only the specified `terms` can be viewed when
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+using the Single Metric Viewer.
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+--
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+end::model-plot-config[]
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+
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+tag::model-snapshot-id[]
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+A numerical character string that uniquely identifies the model snapshot. For
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+example, `1491007364`. For more information about model snapshots, see
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+<<ml-snapshot-resource>>.
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+end::model-snapshot-id[]
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+
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+tag::model-snapshot-retention-days[]
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+The time in days that model snapshots are retained for the job. Older snapshots
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+are deleted. The default value is `1`, which means snapshots are retained for
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+one day (twenty-four hours).
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+end::model-snapshot-retention-days[]
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+
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+tag::multivariate-by-fields[]
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+This functionality is reserved for internal use. It is not supported for use in
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+customer environments and is not subject to the support SLA of official GA
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+features.
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++
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+--
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+If set to `true`, the analysis will automatically find correlations between
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+metrics for a given `by` field value and report anomalies when those
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+correlations cease to hold. For example, suppose CPU and memory usage on host A
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+is usually highly correlated with the same metrics on host B. Perhaps this
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+correlation occurs because they are running a load-balanced application.
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+If you enable this property, then anomalies will be reported when, for example,
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+CPU usage on host A is high and the value of CPU usage on host B is low. That
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+is to say, you'll see an anomaly when the CPU of host A is unusual given
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+the CPU of host B.
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+
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+NOTE: To use the `multivariate_by_fields` property, you must also specify
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+`by_field_name` in your detector.
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+
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+--
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+end::multivariate-by-fields[]
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+
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+tag::over-field-name[]
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+The field used to split the data. In particular, this property is used for
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+analyzing the splits with respect to the history of all splits. It is used for
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+finding unusual values in the population of all splits. For more information,
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+see {stack-ov}/ml-configuring-pop.html[Performing population analysis].
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+end::over-field-name[]
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+
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+tag::partition-field-name[]
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+The field used to segment the analysis. When you use this property, you have
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+completely independent baselines for each value of this field.
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+end::partition-field-name[]
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tag::prediction_field_name[]
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`prediction_field_name`::
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@@ -60,6 +615,63 @@ tag::prediction_field_name[]
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Defaults to `<dependent_variable>_prediction`.
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end::prediction_field_name[]
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+tag::renormalization-window-days[]
|
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+Advanced configuration option. The period over which adjustments to the score
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+are applied, as new data is seen. The default value is the longer of 30 days or
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+100 `bucket_spans`.
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+end::renormalization-window-days[]
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+
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+tag::results-index-name[]
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+A text string that affects the name of the {ml} results index. The default value
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+is `shared`, which generates an index named `.ml-anomalies-shared`.
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+end::results-index-name[]
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+
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+tag::results-retention-days[]
|
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+Advanced configuration option. The number of days for which job results are
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+retained. Once per day at 00:30 (server time), results older than this period
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+are deleted from {es}. The default value is null, which means results are
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+retained.
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+end::results-retention-days[]
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+
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+tag::summary-count-field-name[]
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+If this property is specified, the data that is fed to the job is expected to be
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+pre-summarized. This property value is the name of the field that contains the
|
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+count of raw data points that have been summarized. The same
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+`summary_count_field_name` applies to all detectors in the job.
|
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++
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+--
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+NOTE: The `summary_count_field_name` property cannot be used with the `metric`
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+function.
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+
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+--
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+end::summary-count-field-name[]
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+
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+tag::time-format[]
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+The time format, which can be `epoch`, `epoch_ms`, or a custom pattern. The
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+default value is `epoch`, which refers to UNIX or Epoch time (the number of
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+seconds since 1 Jan 1970). The value `epoch_ms` indicates that time is measured
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+in milliseconds since the epoch. The `epoch` and `epoch_ms` time formats accept
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+either integer or real values. +
|
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++
|
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+--
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+NOTE: Custom patterns must conform to the Java `DateTimeFormatter` class.
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+When you use date-time formatting patterns, it is recommended that you provide
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+the full date, time and time zone. For example: `yyyy-MM-dd'T'HH:mm:ssX`.
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+If the pattern that you specify is not sufficient to produce a complete timestamp,
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+job creation fails.
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+
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+--
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+end::time-format[]
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+
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+tag::tokenizer[]
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+The name or definition of the <<analysis-tokenizers,tokenizer>> to use after
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+character filters are applied. This property is compulsory if
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+`categorization_analyzer` is specified as an object. Machine learning provides a
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+tokenizer called `ml_classic` that tokenizes in the same way as the
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+non-customizable tokenizer in older versions of the product. If you want to use
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+that tokenizer but change the character or token filters, specify
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+`"tokenizer": "ml_classic"` in your `categorization_analyzer`.
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+end::tokenizer[]
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|
tag::training_percent[]
|
|
|
`training_percent`::
|
|
|
@@ -67,4 +679,9 @@ tag::training_percent[]
|
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be used for training. Documents that are ignored by the analysis (for example
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those that contain arrays) won’t be included in the calculation for used
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percentage. Defaults to `100`.
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-end::training_percent[]
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+end::training_percent[]
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+
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+tag::use-null[]
|
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+Defines whether a new series is used as the null series when there is no value
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+for the by or partition fields. The default value is `false`.
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+end::use-null[]
|
Lisa Cawley