elasticsearch/docs/reference/ml/ml-shared.asciidoc

tag::aggregations[]
If set, the {dfeed} performs aggregation searches. Support for aggregations is
limited and should be used only with low cardinality data. For more information,
see
{ml-docs}/ml-configuring-aggregation.html[Aggregating data for faster performance].
end::aggregations[]

tag::allow-lazy-open[]
Advanced configuration option. Specifies whether this job can open when there is 
insufficient {ml} node capacity for it to be immediately assigned to a node. The
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 
error. However, this is also subject to the cluster-wide 
`xpack.ml.max_lazy_ml_nodes` setting; see <<advanced-ml-settings>>. If this
option is set to `true`, the <<ml-open-job,open {anomaly-jobs} API>> does not
return an error and the job waits in the `opening` state until sufficient {ml}
node capacity is available.
end::allow-lazy-open[]

tag::allow-lazy-start[]
Whether this job should be allowed to start when there is insufficient {ml} node 
capacity for it to be immediately assigned to a node. The default is `false`, 
which means that the <<start-dfanalytics>> will return an error if a {ml} node 
with capacity to run the job cannot immediately be found. (However, this is also 
subject to the cluster-wide `xpack.ml.max_lazy_ml_nodes` setting - see 
<<advanced-ml-settings>>.) If this option is set to `true` then the 
<<start-dfanalytics>> will not return an error, and the job will wait in the 
`starting` state until sufficient {ml} node capacity is available.
end::allow-lazy-start[]

tag::allow-no-datafeeds[]
Specifies what to do when the request:
+
--
* Contains wildcard expressions and there are no {dfeeds} that match.
* Contains the `_all` string or no identifiers and there are no matches.
* Contains wildcard expressions and there are only partial matches. 

The default value is `true`, which returns an empty `datafeeds` array when
there are no matches and the subset of results when there are partial matches.
If this parameter is `false`, the request returns a `404` status code when there
are no matches or only partial matches.
--
end::allow-no-datafeeds[]

tag::allow-no-jobs[]
Specifies what to do when the request:
+
--
* Contains wildcard expressions and there are no jobs that match.
* Contains the `_all` string or no identifiers and there are no matches.
* Contains wildcard expressions and there are only partial matches.

The default value is `true`, which returns an empty `jobs` array 
when there are no matches and the subset of results when there are partial 
matches. If this parameter is `false`, the request returns a `404` status code
when there are no matches or only partial matches.
--
end::allow-no-jobs[]

tag::allow-no-match[]
 Specifies what to do when the request:
+
--
* Contains wildcard expressions and there are no {dfanalytics-jobs} that match.
* Contains the `_all` string or no identifiers and there are no matches.
* Contains wildcard expressions and there are only partial matches. 

The default value is `true`, which returns an empty `data_frame_analytics` array 
when there are no matches and the subset of results when there are partial 
matches. If this parameter is `false`, the request returns a `404` status code 
when there are no matches or only partial matches.
--
end::allow-no-match[]

tag::analysis[]
Defines the type of {dfanalytics} you want to perform on your source index. For 
example: `outlier_detection`. See <<ml-dfa-analysis-objects>>.
end::analysis[]

tag::analysis-config[]
The analysis configuration, which specifies how to analyze the data. After you
create a job, you cannot change the analysis configuration; all the properties
are informational.
end::analysis-config[]

tag::analysis-limits[]
Limits can be applied for the resources required to hold the mathematical models
in memory. These limits are approximate and can be set per job. They do not
control the memory used by other processes, for example the {es} Java processes.
end::analysis-limits[]

tag::assignment-explanation-anomaly-jobs[]
For open {anomaly-jobs} only, contains messages relating to the selection
of a node to run the job.
end::assignment-explanation-anomaly-jobs[]

tag::assignment-explanation-datafeeds[]
For started {dfeeds} only, contains messages relating to the selection of a 
node.
end::assignment-explanation-datafeeds[]

tag::assignment-explanation-dfanalytics[]
Contains messages relating to the selection of a node.
end::assignment-explanation-dfanalytics[]

tag::background-persist-interval[]
Advanced configuration option. The time between each periodic persistence of the 
model. The default value is a randomized value between 3 to 4 hours, which
avoids all jobs persisting at exactly the same time. The smallest allowed value
is 1 hour.
+
--
TIP: For very large models (several GB), persistence could take 10-20 minutes,
so do not set the `background_persist_interval` value too low.

--
end::background-persist-interval[]

tag::bucket-allocation-failures-count[]
The number of buckets for which new entities in incoming data were not processed
due to insufficient model memory. This situation is also signified by a
`hard_limit: memory_status` property value.
end::bucket-allocation-failures-count[]

tag::bucket-count[]
The number of buckets processed.
end::bucket-count[]

tag::bucket-count-anomaly-jobs[]
The number of bucket results produced by the job.
end::bucket-count-anomaly-jobs[]

tag::bucket-span[]
The size of the interval that the analysis is aggregated into, typically between
`5m` and `1h`. The default value is `5m`. If the {anomaly-job} uses a {dfeed}
with {ml-docs}/ml-configuring-aggregation.html[aggregations], this value must be
divisible by the interval of the date histogram aggregation. For more
information, see {ml-docs}/ml-buckets.html[Buckets]. 
end::bucket-span[]

tag::bucket-span-results[]
The length of the bucket in seconds. This value matches the `bucket_span`
that is specified in the job.
end::bucket-span-results[]

tag::bucket-time-exponential-average[]
Exponential moving average of all bucket processing times, in milliseconds.
end::bucket-time-exponential-average[]

tag::bucket-time-exponential-average-hour[]
Exponentially-weighted moving average of bucket processing times
calculated in a 1 hour time window, in milliseconds.
end::bucket-time-exponential-average-hour[]

tag::bucket-time-maximum[]
Maximum among all bucket processing times, in milliseconds.
end::bucket-time-maximum[]

tag::bucket-time-minimum[]
Minimum among all bucket processing times, in milliseconds.
end::bucket-time-minimum[]

tag::bucket-time-total[]
Sum of all bucket processing times, in milliseconds.
end::bucket-time-total[]

tag::by-field-name[]
The field used to split the data. In particular, this property is used for 
analyzing the splits with respect to their own history. It is used for finding 
unusual values in the context of the split.
end::by-field-name[]

tag::calendar-id[]
A string that uniquely identifies a calendar.
end::calendar-id[]

tag::categorization-analyzer[]
If `categorization_field_name` is specified, you can also define the analyzer
that is used to interpret the categorization field. This property cannot be used
at the same time as `categorization_filters`. The categorization analyzer
specifies how the `categorization_field` is interpreted by the categorization
process. The syntax is very similar to that used to define the `analyzer` in the
<<indices-analyze,Analyze endpoint>>. For more information, see
{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
+
The `categorization_analyzer` field can be specified either as a string or as an
object. If it is a string it must refer to a
<<analysis-analyzers,built-in analyzer>> or one added by another plugin. If it
is an object it has the following properties:
+
.Properties of `categorization_analyzer`
[%collapsible%open]
=====
`char_filter`::::
(array of strings or objects)
include::{docdir}/ml/ml-shared.asciidoc[tag=char-filter]

`tokenizer`::::
(string or object)
include::{docdir}/ml/ml-shared.asciidoc[tag=tokenizer]

`filter`::::
(array of strings or objects)
include::{docdir}/ml/ml-shared.asciidoc[tag=filter]
=====
end::categorization-analyzer[]

tag::categorization-examples-limit[]
The maximum number of examples stored per category in memory and in the results
data store. The default value is 4.  If you increase this value, more examples
are available, however it requires that you have more storage available. If you
set this value to `0`, no examples are stored.
+
NOTE: The `categorization_examples_limit` only applies to analysis that uses
categorization. For more information, see
{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
end::categorization-examples-limit[]

tag::categorization-field-name[]
If this property is specified, the values of the specified field will be 
categorized. The resulting categories must be used in a detector by setting
`by_field_name`, `over_field_name`, or `partition_field_name` to the keyword
`mlcategory`. For more information, see
{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
end::categorization-field-name[]

tag::categorization-filters[]
If `categorization_field_name` is specified, you can also define optional
filters. This property expects an array of regular expressions. The expressions
are used to filter out matching sequences from the categorization field values.
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 
{ml-docs}/ml-configuring-categories.html[Categorizing log messages]. This
property cannot be used at the same time as `categorization_analyzer`. If you
only want to define simple regular expression filters that are applied prior to 
tokenization, setting this property is the easiest method. If you also want to 
customize the tokenizer or post-tokenization filtering, use the 
`categorization_analyzer` property instead and include the filters as
`pattern_replace` character filters. The effect is exactly the same.
end::categorization-filters[]

tag::categorization-status[]
The status of categorization for the job. Contains one of the following values:
+
--
* `ok`: Categorization is performing acceptably well (or not being used at all).
* `warn`: Categorization is detecting a distribution of categories that suggests
the input data is inappropriate for categorization. Problems could be that there
is only one category, more than 90% of categories are rare, the number of
categories is greater than 50% of the number of categorized documents, there are
no frequently matched categories, or more than 50% of categories are dead.

--
end::categorization-status[]

tag::categorized-doc-count[]
The number of documents that have had a field categorized.
end::categorized-doc-count[]

tag::char-filter[]
One or more <<analysis-charfilters,character filters>>. In addition to the
built-in character filters, other plugins can provide more character filters.
This property is optional. If it is not specified, no character filters are 
applied prior to categorization. If you are customizing some other aspect of the
analyzer and you need to achieve the equivalent of `categorization_filters`
(which are not permitted when some other aspect of the analyzer is customized),
add them here as
<<analysis-pattern-replace-charfilter,pattern replace character filters>>.
end::char-filter[]

tag::compute-feature-influence[]
If `true`, the feature influence calculation is enabled. Defaults to `true`.
end::compute-feature-influence[]

tag::chunking-config[]
{dfeeds-cap} might be required to search over long time periods, for several 
months or years. This search is split into time chunks in order to ensure the 
load on {es} is managed. Chunking configuration controls how the size of these 
time chunks are calculated and is an advanced configuration option.
+
.Properties of `chunking_config`
[%collapsible%open]
====
`mode`:::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=mode]

`time_span`:::
(<<time-units,time units>>)
include::{docdir}/ml/ml-shared.asciidoc[tag=time-span]
====
end::chunking-config[]

tag::class-assignment-objective[]
Defines the objective to optimize when assigning class labels. Available
objectives are `maximize_accuracy` and `maximize_minimum_recall`. When maximizing
accuracy class labels are chosen to maximize the number of correct predictions.
When maximizing minimum recall labels are chosen to maximize the minimum recall
for any class. Defaults to maximize_minimum_recall.
end::class-assignment-objective[]

tag::custom-rules[]
An array of custom rule objects, which enable you to customize the way detectors
operate. For example, a rule may dictate to the detector conditions under which
results should be skipped. For more examples, see 
{ml-docs}/ml-configuring-detector-custom-rules.html[Customizing detectors with custom rules].
end::custom-rules[]

tag::custom-rules-actions[]
The set of actions to be triggered when the rule applies. If
more than one action is specified the effects of all actions are combined. The
available actions include:

* `skip_result`: The result will not be created. This is the default value.
Unless you also specify `skip_model_update`, the model will be updated as usual
with the corresponding series value.
* `skip_model_update`: The value for that series will not be used to update the
model. Unless you also specify `skip_result`, the results will be created as
usual. This action is suitable when certain values are expected to be
consistently anomalous and they affect the model in a way that negatively
impacts the rest of the results.
end::custom-rules-actions[]

tag::custom-rules-scope[]
An optional scope of series where the rule applies. A rule must either
have a non-empty scope or at least one condition. By default, the scope includes
all series. Scoping is allowed for any of the fields that are also specified in
`by_field_name`, `over_field_name`, or `partition_field_name`. To add a scope
for a field, add the field name as a key in the scope object and set its value
to an object with the following properties:
end::custom-rules-scope[]

tag::custom-rules-scope-filter-id[]
The id of the filter to be used.
end::custom-rules-scope-filter-id[]

tag::custom-rules-scope-filter-type[]
Either `include` (the rule applies for values in the filter) or `exclude` (the
rule applies for values not in the filter). Defaults to `include`.
end::custom-rules-scope-filter-type[]

tag::custom-rules-conditions[]
An optional array of numeric conditions when the rule applies. A rule must
either have a non-empty scope or at least one condition. Multiple conditions are
combined together with a logical `AND`. A condition has the following 
properties:
end::custom-rules-conditions[]

tag::custom-rules-conditions-applies-to[]
Specifies the result property to which the condition applies. The available
options are `actual`, `typical`, `diff_from_typical`, `time`. If your detector
uses `lat_long`, `metric`, `rare`, or `freq_rare` functions, you can only
specify conditions that apply to `time`.
end::custom-rules-conditions-applies-to[]

tag::custom-rules-conditions-operator[]
Specifies the condition operator. The available options are `gt` (greater than),
`gte` (greater than or equals), `lt` (less than) and `lte` (less than or 
equals).
end::custom-rules-conditions-operator[]

tag::custom-rules-conditions-value[]
The value that is compared against the `applies_to` field using the `operator`.
end::custom-rules-conditions-value[]

tag::custom-settings[]
Advanced configuration option. Contains custom meta data about the job. For
example, it can contain custom URL information as shown in
{ml-docs}/ml-configuring-url.html[Adding custom URLs to {ml} results].
end::custom-settings[]

tag::data-description[]
The data description defines the format of the input data when you send data to
the job by using the <<ml-post-data,post data>> API. Note that when configure
a {dfeed}, these properties are automatically set. When data is received via 
the <<ml-post-data,post data>> API, it is not stored in {es}. Only the results 
for {anomaly-detect} are retained.
+
.Properties of `data_description`
[%collapsible%open]
====
`format`:::
  (string) Only `JSON` format is supported at this time.

`time_field`:::
  (string) The name of the field that contains the timestamp.
  The default value is `time`.

`time_format`:::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=time-format]
====
end::data-description[]

tag::data-frame-analytics[]
An array of {dfanalytics-job} resources, which are sorted by the `id` value in 
ascending order.
+
.Properties of {dfanalytics-job} resources
[%collapsible%open]
====
`analysis`:::
(object) The type of analysis that is performed on the `source`.

//Begin analyzed_fields
`analyzed_fields`:::
(object) Contains `includes` and/or `excludes` patterns that select which fields 
are included in the analysis.
+
.Properties of `analyzed_fields`
[%collapsible%open]
=====
`excludes`:::
(Optional, array) An array of strings that defines the fields that are excluded 
from the analysis.
    
`includes`:::
(Optional, array) An array of strings that defines the fields that are included 
in the analysis.
=====
//End analyzed_fields
//Begin dest
`dest`:::
(string) The destination configuration of the analysis.
+
.Properties of `dest`
[%collapsible%open]
=====
`index`:::
(string) The _destination index_ that stores the results of the 
{dfanalytics-job}.

`results_field`:::
(string) The name of the field that stores the results of the analysis. Defaults 
to `ml`.
=====
//End dest

`id`:::
(string) The unique identifier of the {dfanalytics-job}.

`model_memory_limit`:::
(string) The `model_memory_limit` that has been set to the {dfanalytics-job}.

`source`:::
(object) The configuration of how the analysis data is sourced. It has an 
`index` parameter and optionally a `query` and a `_source`.
+
.Properties of `source`
[%collapsible%open]
=====
`index`:::
(array) Index or indices on which to perform the analysis. It can be a single 
index or index pattern as well as an array of indices or patterns.
    
`query`:::
(object) The query that has been specified for the {dfanalytics-job}. The {es} 
query domain-specific language (<<query-dsl,DSL>>). This value corresponds to 
the query object in an {es} search POST body. By default, this property has the 
following value: `{"match_all": {}}`.

`_source`:::
(object) Contains the specified `includes` and/or `excludes` patterns that 
select which fields are present in the destination. Fields that are excluded 
cannot be included in the analysis.
+
.Properties of `_source`
[%collapsible%open]
======
`excludes`:::
(array) An array of strings that defines the fields that are excluded from the 
destination.
        
`includes`:::
(array) An array of strings that defines the fields that are included in the 
destination.
======
//End of _source
=====
//End source
====
end::data-frame-analytics[]

tag::data-frame-analytics-stats[]
An array of statistics objects for {dfanalytics-jobs}, which are
sorted by the `id` value in ascending order.

//Begin analysis_stats
`analysis_stats`::
(object)
An object containing statistical data about the analysis.
+
.Properties of `analysis_stats`
[%collapsible%open]
====
//Begin classification_stats
`classification_stats`:::
(object)
An object containing statistical data about the {classanalysis}.
+
.Properties of `classification_stats`
[%collapsible%open]
=====
//Begin class_hyperparameters
`hyperparameters`::::
(object)
An object containing the parameters of the {classanalysis}.
+
.Properties of `hyperparameters`
[%collapsible%open]
======
tag::dfas-alpha[]
`alpha`::::
(double)
Regularization factor to penalize deeper trees when training decision trees.
end::dfas-alpha[]

`class_assignment_objective`::::
(string)
Defines whether class assignment maximizes the accuracy or the minimum recall 
metric. Possible values are `maximize_accuracy` and `maximize_minimum_recall`.

tag::dfas-downsample-factor[]
`downsample_factor`::::
(double)
The value of the downsample factor.
end::dfas-downsample-factor[]

tag::dfas-eta[]
`eta`::::
(double)
The value of the eta hyperparameter.
end::dfas-eta[]

tag::dfas-eta-growth[]
`eta_growth_rate_per_tree`::::
(double)
Specifies the rate at which the `eta` increases for each new tree that is added to the 
forest. For example, a rate of `1.05` increases `eta` by 5%.
end::dfas-eta-growth[]

tag::dfas-feature-bag-fraction[]
`feature_bag_fraction`::::
(double)
The fraction of features that is used when selecting a random bag for each 
candidate split.
end::dfas-feature-bag-fraction[]

tag::dfas-gamma[]
`gamma`::::
(double)
Regularization factor to penalize trees with large numbers of nodes.
end::dfas-gamma[]

tag::dfas-lambda[]
`lambda`::::
(double)
Regularization factor to penalize large leaf weights.
end::dfas-lambda[]

tag::dfas-max-attempts[]
`max_attempts_to_add_tree`::::
(integer)
If the algorithm fails to determine a non-trivial tree (more than a single 
leaf), this parameter determines how many of such consecutive failures are 
tolerated. Once the number of attempts exceeds the threshold, the forest 
training stops.
end::dfas-max-attempts[]

tag::dfas-max-optimization-rounds[]
`max_optimization_rounds_per_hyperparameter`::::
(integer)
A multiplier responsible for determining the maximum number of 
hyperparameter optimization steps in the Bayesian optimization procedure. 
The maximum number of steps is determined based on the number of undefined hyperparameters 
times the maximum optimization rounds per hyperparameter.
end::dfas-max-optimization-rounds[]

tag::dfas-max-trees[]
`max_trees`::::
(integer)
The maximum number of trees in the forest.
end::dfas-max-trees[]

tag::dfas-num-folds[]
`num_folds`::::
(integer)
The maximum number of folds for the cross-validation procedure.
end::dfas-num-folds[]

tag::dfas-num-splits[]
`num_splits_per_feature`::::
(integer)
Determines the maximum number of splits for every feature that can occur in a 
decision tree when the tree is trained.
end::dfas-num-splits[]

tag::dfas-soft-limit[]
`soft_tree_depth_limit`::::
(double)
Tree depth limit is used for calculating the tree depth penalty. This is a soft 
limit, it can be exceeded.
end::dfas-soft-limit[]

tag::dfas-soft-tolerance[]
`soft_tree_depth_tolerance`::::
(double)
Tree depth tolerance is used for calculating the tree depth penalty. This is a 
soft limit, it can be exceeded.
end::dfas-soft-tolerance[]
======
//End class_hyperparameters

tag::dfas-iteration[]
`iteration`::::
(integer)
The number of iterations on the analysis.
end::dfas-iteration[]

tag::dfas-timestamp[]
`timestamp`::::
(date)
The timestamp when the statistics were reported in milliseconds since the epoch.
end::dfas-timestamp[]

//Begin class_timing_stats
tag::dfas-timing-stats[]
`timing_stats`::::
(object)
An object containing time statistics about the {dfanalytics-job}.
end::dfas-timing-stats[]
+
.Properties of `timing_stats`
[%collapsible%open]
======
tag::dfas-timing-stats-elapsed[]
`elapsed_time`::::
(integer)
Runtime of the analysis in milliseconds.
end::dfas-timing-stats-elapsed[]

tag::dfas-timing-stats-iteration[]
`iteration_time`::::
(integer)
Runtime of the latest iteration of the analysis in milliseconds.
end::dfas-timing-stats-iteration[]
======
//End class_timing_stats

//Begin class_validation_loss
tag::dfas-validation-loss[]
`validation_loss`::::
(object)
An object containing information about validation loss.
end::dfas-validation-loss[]
+
.Properties of `validation_loss`
[%collapsible%open]
======
tag::dfas-validation-loss-type[]
`loss_type`::::
(string)
The type of the loss metric. For example, `binomial_logistic`.
end::dfas-validation-loss-type[]

tag::dfas-validation-loss-fold[]
`fold_values`::::
(array of strings)
Validation loss values for every added decision tree during the forest growing 
procedure.
end::dfas-validation-loss-fold[]
======
//End class_validation_loss
=====
//End classification_stats

//Begin outlier_detection_stats
`outlier_detection_stats`:::
(object)
An object containing statistical data about the {oldetection} job.
+
.Properties of `outlier_detection_stats`
[%collapsible%open]
=====
//Begin parameters
`parameters`::::
(object)
The list of job parameters specified by the user or determined by algorithmic 
heuristics.
+
.Properties of `parameters`
[%collapsible%open]
======
`compute_feature_influence`::::
(boolean)
If true, feature influence calculation is enabled.

`feature_influence_threshold`::::
(double)
The minimum {olscore} that a document needs to have to calculate its feature 
influence score.

`method`::::
(string)
The method that {oldetection} uses. Possible values are `lof`, `ldof`, 
`distance_kth_nn`, `distance_knn`, and `ensemble`.

`n_neighbors`::::
(integer)
The value for how many nearest neighbors each method of {oldetection} uses to 
calculate its outlier score.

`outlier_fraction`::::
(double)
The proportion of the data set that is assumed to be outlying prior to 
{oldetection}.

`standardization_enabled`::::
(boolean)
If true, then the following operation is performed on the columns before 
computing {olscores}: (x_i - mean(x_i)) / sd(x_i).
======
//End parameters

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timestamp]

//Begin od_timing_stats
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats]
+
.Property of `timing_stats`
[%collapsible%open]
======
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats-elapsed]
======
//End od_timing_stats
=====
//End outlier_detection_stats

//Begin regression_stats
`regression_stats`:::
(object)
An object containing statistical data about the {reganalysis}.
+
.Properties of `regression_stats`
[%collapsible%open]
=====
//Begin reg_hyperparameters
`hyperparameters`::::
(object)
An object containing the parameters of the {reganalysis}.
+
.Properties of `hyperparameters`
[%collapsible%open]
======
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-alpha]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-downsample-factor]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-eta]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-eta-growth]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-feature-bag-fraction]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-gamma]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-lambda]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-attempts]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-optimization-rounds]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-trees]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-num-folds]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-num-splits]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-soft-limit]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-soft-tolerance]
======
//End reg_hyperparameters

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-iteration]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timestamp]

//Begin reg_timing_stats
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats]
+
.Propertis of `timing_stats`
[%collapsible%open]
======
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats-elapsed]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats-iteration]
======
//End reg_timing_stats

//Begin reg_validation_loss
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss]
+
.Properties of `validation_loss`
[%collapsible%open]
======
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss-type]

include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss-fold]
======
//End reg_validation_loss
=====
//End regression_stats
====
//End analysis_stats

`assignment_explanation`:::
(string)
For running jobs only, contains messages relating to the selection of a node to 
run the job.

//Begin data_counts
`data_counts`:::
(object)
An object containing statistical data about the documents in the analysis.
+
.Properties of `data_counts`
[%collapsible%open]
====
`skipped_docs_count`:::
(integer)
The number of documents that are skipped during the analysis because they 
contained values that are not supported by the analysis. For example, 
{oldetection} does not support missing fields so it skips documents with missing 
fields. Likewise, all types of analysis skip documents that contain arrays with 
more than one element.

`test_docs_count`:::
(integer)
The number of documents that are not used for training the model and can be used 
for testing.

`training_docs_count`:::
(integer)
The number of documents that are used for training the model.
====
//End data_counts

`id`:::
(string)
The unique identifier of the {dfanalytics-job}.

`memory_usage`:::
(Optional, object)
An object describing memory usage of the analytics. It is present only after the 
job is started and memory usage is reported.

`memory_usage`.`peak_usage_bytes`:::
(long)
The number of bytes used at the highest peak of memory usage.

`memory_usage`.`timestamp`:::
(date)
The timestamp when memory usage was calculated.

`node`:::
(object)
Contains properties for the node that runs the job. This information is 
available only for running jobs.

`node`.`attributes`:::
(object)
Lists node attributes such as `ml.machine_memory`, `ml.max_open_jobs`, and 
`xpack.installed`.

`node`.`ephemeral_id`:::
(string)
The ephemeral id of the node.

`node`.`id`:::
(string)
The unique identifier of the node.

`node`.`name`:::
(string)
The node name.

`node`.`transport_address`:::
(string)
The host and port where transport HTTP connections are accepted.

`progress`:::
(array) The progress report of the {dfanalytics-job} by phase.
  
`progress`.`phase`:::
(string) Defines the phase of the {dfanalytics-job}. Possible phases: 
`reindexing`, `loading_data`, `analyzing`, and `writing_results`.
    
`progress`.`progress_percent`:::
(integer) The progress that the {dfanalytics-job} has made expressed in 
percentage.

`state`:::
(string) Current state of the {dfanalytics-job}.
end::data-frame-analytics-stats[]

tag::datafeed-id[]
A numerical character string that uniquely identifies the
{dfeed}. This identifier can contain lowercase alphanumeric characters (a-z
and 0-9), hyphens, and underscores. It must start and end with alphanumeric
characters.
end::datafeed-id[]

tag::datafeed-id-wildcard[]
Identifier for the {dfeed}. It can be a {dfeed} identifier or a wildcard
expression.
end::datafeed-id-wildcard[]

tag::dead-category-count[]
The number of categories created by categorization that will never be assigned
again because another category's definition makes it a superset of the dead
category. (Dead categories are a side effect of the way categorization has no
prior training.)
end::dead-category-count[]

tag::decompress-definition[]
Specifies whether the included model definition should be returned as a JSON map 
(`true`) or in a custom compressed format (`false`). Defaults to `true`.
end::decompress-definition[]

tag::delayed-data-check-config[]
Specifies whether the {dfeed} checks for missing data and the size of the
window. For example: `{"enabled": true, "check_window": "1h"}`.
+
The {dfeed} can optionally search over indices that have already been read in
an effort to determine whether any data has subsequently been added to the 
index. If missing data is found, it is a good indication that the `query_delay` 
option is set too low and the data is being indexed after the {dfeed} has passed 
that moment in time. See 
{ml-docs}/ml-delayed-data-detection.html[Working with delayed data].
+
This check runs only on real-time {dfeeds}.
+
.Properties of `delayed_data_check_config`
[%collapsible%open]
====
`check_window`::
(<<time-units,time units>>) The window of time that is searched for late data.
This window of time ends with the latest finalized bucket. It defaults to
`null`, which causes an appropriate `check_window` to be calculated when the
real-time {dfeed} runs. In particular, the default `check_window` span
calculation is based on the maximum of `2h` or `8 * bucket_span`.

`enabled`::
(boolean) Specifies whether the {dfeed} periodically checks for delayed data.
Defaults to `true`.
====
end::delayed-data-check-config[]

tag::dependent-variable[]
Defines which field of the document is to be predicted.
This parameter is supplied by field name and must match one of the fields in 
the index being used to train. If this field is missing from a document, then 
that document will not be used for training, but a prediction with the trained 
model will be generated for it. It is also known as continuous target variable.
end::dependent-variable[]

tag::desc-results[]
If true, the results are sorted in descending order.
end::desc-results[]

tag::description-dfa[]
A description of the job.
end::description-dfa[]

tag::dest[]
The destination configuration, consisting of `index` and optionally
`results_field` (`ml` by default).
+
.Properties of `dest`
[%collapsible%open]
====
`index`:::
(Required, string) Defines the _destination index_ to store the results of the
{dfanalytics-job}.
  
`results_field`:::
(Optional, string) Defines the name of the field in which to store the results
of the analysis. Defaults to `ml`.
====
end::dest[]

tag::detector-description[]
A description of the detector. For example, `Low event rate`.
end::detector-description[]

tag::detector-field-name[]
The field that the detector uses in the function. If you use an event rate 
function such as `count` or `rare`, do not specify this field.
+
--
NOTE: The `field_name` cannot contain double quotes or backslashes.

--
end::detector-field-name[]

tag::detector-index[]
A unique identifier for the detector. This identifier is based on the order of 
the detectors in the `analysis_config`, starting at zero.
end::detector-index[]

tag::earliest-record-timestamp[]
The timestamp of the earliest chronologically input document.
end::earliest-record-timestamp[]

tag::empty-bucket-count[]
The number of buckets which did not contain any data. If your data
contains many empty buckets, consider increasing your `bucket_span` or using
functions that are tolerant to gaps in data such as `mean`, `non_null_sum` or
`non_zero_count`.
end::empty-bucket-count[]

tag::eta[]
Advanced configuration option. The shrinkage applied to the weights. Smaller
values result in larger forests which have better generalization error. However,
the smaller the value the longer the training will take. For more information
about shrinkage, see
https://en.wikipedia.org/wiki/Gradient_boosting#Shrinkage[this wiki article].
end::eta[]

tag::exclude-frequent[]
Contains one of the following values: `all`, `none`, `by`, or `over`. If set, 
frequent entities are excluded from influencing the anomaly results. Entities
can be considered frequent over time or frequent in a population. If you are 
working with both over and by fields, then you can set `exclude_frequent` to 
`all` for both fields, or to `by` or `over` for those specific fields.
end::exclude-frequent[]

tag::exclude-interim-results[]
If `true`, the output excludes interim results. By default, interim results are 
included.
end::exclude-interim-results[]

tag::feature-bag-fraction[]
Advanced configuration option. Defines the fraction of features that will be
used when selecting a random bag for each candidate split. 
end::feature-bag-fraction[]

tag::feature-influence-threshold[]
The minimum {olscore} that a document needs to have in order to calculate its 
{fiscore}. Value range: 0-1 (`0.1` by default).
end::feature-influence-threshold[]

tag::field-selection[]
An array of objects that explain selection for each field, sorted by 
the field names.
+
.Properties of `field_selection` objects
[%collapsible%open]
====
`is_included`:::
(boolean) Whether the field is selected to be included in the analysis.

`is_required`:::
(boolean) Whether the field is required.

`feature_type`:::
(string) The feature type of this field for the analysis. May be `categorical` 
or `numerical`.

`mapping_types`:::
(string) The mapping types of the field.

`name`:::
(string) The field name.

`reason`:::
(string) The reason a field is not selected to be included in the analysis.
====
end::field-selection[]

tag::filter[]
One or more <<analysis-tokenfilters,token filters>>. In addition to the built-in 
token filters, other plugins can provide more token filters. This property is
optional. If it is not specified, no token filters are applied prior to
categorization.
end::filter[]

tag::filter-id[]
A string that uniquely identifies a filter.
end::filter-id[]

tag::forecast-total[]
The number of individual forecasts currently available for the job. A value of
`1` or more indicates that forecasts exist.
end::forecast-total[]

tag::frequency[]
The interval at which scheduled queries are made while the {dfeed} runs in real
time. The default value is either the bucket span for short bucket spans, or,
for longer bucket spans, a sensible fraction of the bucket span. For example:
`150s`. When `frequency` is shorter than the bucket span, interim results for
the last (partial) bucket are written then eventually overwritten by the full
bucket results. If the {dfeed} uses aggregations, this value must be divisible
by the interval of the date histogram aggregation.
end::frequency[]

tag::frequent-category-count[]
The number of categories that match more than 1% of categorized documents.
end::frequent-category-count[]

tag::from[]
Skips the specified number of {dfanalytics-jobs}. The default value is `0`.
end::from[]

tag::function[]
The analysis function that is used. For example, `count`, `rare`, `mean`, `min`, 
`max`, and `sum`. For more information, see
{ml-docs}/ml-functions.html[Function reference].
end::function[]

tag::gamma[]
Advanced configuration option. Regularization parameter to prevent overfitting
on the training dataset. Multiplies a linear penalty associated with the size of 
individual trees in the forest. The higher the value the more training will 
prefer smaller trees. The smaller this parameter the larger individual trees
will be and the longer train will take.
end::gamma[]

tag::groups[]
A list of job groups. A job can belong to no groups or many.
end::groups[]

tag::include-model-definition[]
Specifies if the model definition should be returned in the response. Defaults 
to `false`. When `true`, only a single model must match the ID patterns 
provided, otherwise a bad request is returned.
end::include-model-definition[]

tag::indices[]
An array of index names. Wildcards are supported. For example:
`["it_ops_metrics", "server*"]`.
+
--
NOTE: If any indices are in remote clusters then `node.remote_cluster_client`
must not be set to `false` on any {ml} nodes.

--
end::indices[]

tag::indices-options[]
Specifies index expansion options that are used during search.
+
--
For example:
```
{
   "expand_wildcards": ["all"],
   "ignore_unavailable": true,
   "allow_no_indices": "false",
   "ignore_throttled": true
}
```
For more information about these options, see <<multi-index>>.
--
end::indices-options[]

tag::inference-config-classification-num-top-classes[]
Specifies the number of top class predictions to return. Defaults to 0.
end::inference-config-classification-num-top-classes[]

tag::inference-config-classification-num-top-feature-importance-values[]
Specifies the maximum number of
{ml-docs}/dfa-classification.html#dfa-classification-feature-importance[feature
importance] values per document. By default, it is zero and no feature
importance calculation occurs.
end::inference-config-classification-num-top-feature-importance-values[]

tag::inference-config-classification-top-classes-results-field[]
Specifies the field to which the top classes are written. Defaults to
`top_classes`.
end::inference-config-classification-top-classes-results-field[]

tag::inference-config-regression-num-top-feature-importance-values[]
Specifies the maximum number of
{ml-docs}/dfa-regression.html#dfa-regression-feature-importance[feature
importance] values per document. By default, it is zero and no feature importance
calculation occurs.
end::inference-config-regression-num-top-feature-importance-values[]

tag::inference-config-results-field[]
The field that is added to incoming documents to contain the inference
prediction. Defaults to `predicted_value`.
end::inference-config-results-field[]

tag::influencers[]
A comma separated list of influencer field names. Typically these can be the by, 
over, or partition fields that are used in the detector configuration. You might 
also want to use a field name that is not specifically named in a detector, but 
is available as part of the input data. When you use multiple detectors, the use 
of influencers is recommended as it aggregates results for each influencer 
entity.
end::influencers[]

tag::input-bytes[]
The number of bytes of input data posted to the {anomaly-job}.
end::input-bytes[]

tag::input-field-count[]
The total number of fields in input documents posted to the {anomaly-job}. This
count includes fields that are not used in the analysis. However, be aware that
if you are using a {dfeed}, it extracts only the required fields from the
documents it retrieves before posting them to the job.
end::input-field-count[]

tag::input-record-count[]
The number of input documents posted to the {anomaly-job}.
end::input-record-count[]

tag::invalid-date-count[]
The number of input documents with either a missing date field or a date that
could not be parsed.
end::invalid-date-count[]

tag::is-interim[]
If `true`, this is an interim result. In other words, the results are calculated
based on partial input data.
end::is-interim[]

tag::job-id-anomaly-detection[]
Identifier for the {anomaly-job}.
end::job-id-anomaly-detection[]

tag::job-id-data-frame-analytics[]
Identifier for the {dfanalytics-job}.
end::job-id-data-frame-analytics[]

tag::job-id-anomaly-detection-default[]
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}.
end::job-id-anomaly-detection-default[]

tag::job-id-data-frame-analytics-default[]
Identifier for the {dfanalytics-job}. If you do not specify this option, the API
returns information for the first hundred {dfanalytics-jobs}.
end::job-id-data-frame-analytics-default[]

tag::job-id-anomaly-detection-list[]
An identifier for the {anomaly-jobs}. It can be a job
identifier, a group name, or a comma-separated list of jobs or groups.
end::job-id-anomaly-detection-list[]

tag::job-id-anomaly-detection-wildcard[]
Identifier for the {anomaly-job}. It can be a job identifier, a group name, or a 
wildcard expression.
end::job-id-anomaly-detection-wildcard[]

tag::job-id-anomaly-detection-wildcard-list[]
Identifier for the {anomaly-job}. It can be a job identifier, a group name, a
comma-separated list of jobs or groups, or a wildcard expression.
end::job-id-anomaly-detection-wildcard-list[]

tag::job-id-anomaly-detection-define[]
Identifier for the {anomaly-job}. This identifier can contain lowercase 
alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start 
and end with alphanumeric characters.
end::job-id-anomaly-detection-define[]

tag::job-id-data-frame-analytics-define[]
Identifier for the {dfanalytics-job}. This identifier can contain lowercase 
alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start
and end with alphanumeric characters.
end::job-id-data-frame-analytics-define[]

tag::job-id-datafeed[]
The unique identifier for the job to which the {dfeed} sends data.
end::job-id-datafeed[]

tag::lambda[]
Advanced configuration option. Regularization parameter to prevent overfitting
on the training dataset. Multiplies an L2 regularisation term which applies to
leaf weights of the individual trees in the forest. The higher the value the
more training will attempt to keep leaf weights small. This makes the prediction  
function smoother at the expense of potentially not being able to capture 
relevant relationships between the features and the {depvar}. The smaller this 
parameter the larger individual trees will be and the longer train will take.
end::lambda[]

tag::last-data-time[]
The timestamp at which data was last analyzed, according to server time.
end::last-data-time[]

tag::latency[]
The size of the window in which to expect data that is out of time order. The 
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>>.
+
--
NOTE: Latency is only applicable when you send data by using
the <<ml-post-data,post data>> API.

--
end::latency[]

tag::latest-empty-bucket-timestamp[]
The timestamp of the last bucket that did not contain any data.
end::latest-empty-bucket-timestamp[]

tag::latest-record-timestamp[]
The timestamp of the latest chronologically input document.
end::latest-record-timestamp[]

tag::latest-sparse-record-timestamp[]
The timestamp of the last bucket that was considered sparse.
end::latest-sparse-record-timestamp[]

tag::max-empty-searches[]
If a real-time {dfeed} has never seen any data (including during any initial
training period) then it will automatically stop itself and close its associated
job after this many real-time searches that return no documents. In other words,
it will stop after `frequency` times `max_empty_searches` of real-time 
operation. If not set then a {dfeed} with no end time that sees no data will 
remain started until it is explicitly stopped. By default this setting is not 
set.
end::max-empty-searches[]

tag::max-trees[]
Advanced configuration option. Defines the maximum number of trees the forest is
allowed to contain. The maximum value is 2000.
end::max-trees[]

tag::memory-estimation[]
An object containing the memory estimates.
+
.Properties of `memory_estimation`
[%collapsible%open]
====
`expected_memory_with_disk`:::
(string) Estimated memory usage under the assumption that overflowing to disk is 
allowed during {dfanalytics}. `expected_memory_with_disk` is usually smaller 
than `expected_memory_without_disk` as using disk allows to limit the main 
memory needed to perform {dfanalytics}.

`expected_memory_without_disk`:::
(string) Estimated memory usage under the assumption that the whole 
{dfanalytics} should happen in memory (i.e. without overflowing to disk).
====
end::memory-estimation[]

tag::method[]
Sets the method that {oldetection} uses. If the method is not set {oldetection} 
uses an ensemble of different methods and normalises and combines their 
individual {olscores} to obtain the overall {olscore}. We recommend to use the 
ensemble method. Available methods are `lof`, `ldof`, `distance_kth_nn`, 
`distance_knn`.
end::method[]

tag::missing-field-count[]
The number of input documents that are missing a field that the {anomaly-job} is
configured to analyze. Input documents with missing fields are still processed
because it is possible that not all fields are missing.
+
--
NOTE: If you are using {dfeeds} or posting data to the job in JSON format, a
high `missing_field_count` is often not an indication of data issues. It is not
necessarily a cause for concern.

--
end::missing-field-count[]

tag::mode[]
There are three available modes: 
+
--
* `auto`: The chunk size is dynamically calculated. This is the default and
recommended value.
* `manual`: Chunking is applied according to the specified `time_span`.
* `off`: No chunking is applied.
--
end::mode[]

tag::model-bytes[]
The number of bytes of memory used by the models. This is the maximum value
since the last time the model was persisted. If the job is closed, this value
indicates the latest size.
end::model-bytes[]

tag::model-bytes-exceeded[]
The number of bytes over the high limit for memory usage at the last allocation
failure.
end::model-bytes-exceeded[]

tag::model-id[]
The unique identifier of the trained {infer} model.
end::model-id[]

tag::model-memory-limit[]
The approximate maximum amount of memory resources that are required for 
analytical processing. Once this limit is approached, data pruning becomes
more aggressive. Upon exceeding this limit, new entities are not modeled. The 
default value for jobs created in version 6.1 and later is `1024mb`.
This value will need to be increased for jobs that are expected to analyze high
cardinality fields, but the default is set to a relatively small size to ensure
that high resource usage is a conscious decision. The default value for jobs
created in versions earlier than 6.1 is `4096mb`.
+
If you specify a number instead of a string, the units are assumed to be MiB.
Specifying a string is recommended for clarity. If you specify a byte size unit
of `b` or `kb` and the number does not equate to a discrete number of megabytes,
it is rounded down to the closest MiB. The minimum valid value is 1 MiB. If you
specify a value less than 1 MiB, an error occurs. For more information about
supported byte size units, see <<byte-units>>.
+
If your `elasticsearch.yml` file contains an `xpack.ml.max_model_memory_limit`
setting, an error occurs when you try to create jobs that have
`model_memory_limit` values greater than that setting. For more information,
see <<ml-settings>>.
end::model-memory-limit[]

tag::model-memory-limit-anomaly-jobs[]
The upper limit for model memory usage, checked on increasing values.
end::model-memory-limit-anomaly-jobs[]

tag::model-memory-limit-dfa[]
The approximate maximum amount of memory resources that are permitted for 
analytical processing. The default value for {dfanalytics-jobs} is `1gb`. If 
your `elasticsearch.yml` file contains an `xpack.ml.max_model_memory_limit` 
setting, an error occurs when you try to create {dfanalytics-jobs} that have 
`model_memory_limit` values greater than that setting. For more information, see 
<<ml-settings>>.
end::model-memory-limit-dfa[]

tag::model-memory-status[]
The status of the mathematical models, which can have one of the following
values:
+
--
* `ok`: The models stayed below the configured value.
* `soft_limit`: The models used more than 60% of the configured memory limit
and older unused models will be pruned to free up space.
* `hard_limit`: The models used more space than the configured memory limit.
As a result, not all incoming data was processed.
--
end::model-memory-status[]

tag::model-plot-config[]
This advanced configuration option stores model information along with the
results. It provides a more detailed view into {anomaly-detect}.
+
--
WARNING: If you enable model plot it can add considerable overhead to the 
performance of the system; it is not feasible for jobs with many entities.

Model plot provides a simplified and indicative view of the model and its 
bounds. It does not display complex features such as multivariate correlations 
or multimodal data. As such, anomalies may occasionally be reported which cannot 
be seen in the model plot.

Model plot config can be configured when the job is created or updated later. It 
must be disabled if performance issues are experienced.
--
end::model-plot-config[]

tag::model-plot-config-enabled[]
If true, enables calculation and storage of the model bounds for each entity
that is being analyzed. By default, this is not enabled.
end::model-plot-config-enabled[]

tag::model-plot-config-terms[]
Limits data collection to this comma separated list of partition or by field
values. If terms are not specified or it is an empty string, no filtering is
applied. For example, "CPU,NetworkIn,DiskWrites". Wildcards are not supported.
Only the specified `terms` can be viewed when using the Single Metric Viewer.
end::model-plot-config-terms[]

tag::model-snapshot-id[]
A numerical character string that uniquely identifies the model snapshot. For 
example, `1575402236000 `.
end::model-snapshot-id[]

tag::model-snapshot-retention-days[]
Advanced configuration option. The period of time (in days) that model snapshots 
are retained. Age is calculated relative to the timestamp of the newest model 
snapshot. The default value is `1`, which means snapshots that are one day 
(twenty-four hours) older than the newest snapshot are deleted.
end::model-snapshot-retention-days[]

tag::model-timestamp[]
The timestamp of the last record when the model stats were gathered.
end::model-timestamp[]

tag::multivariate-by-fields[]
This functionality is reserved for internal use. It is not supported for use in 
customer environments and is not subject to the support SLA of official GA 
features.
+
--
If set to `true`, the analysis will automatically find correlations between 
metrics for a given `by` field value and report anomalies when those
correlations cease to hold. For example, suppose CPU and memory usage on host A
is usually highly correlated with the same metrics on host B. Perhaps this
correlation occurs because they are running a load-balanced application.
If you enable this property, then anomalies will be reported when, for example,
CPU usage on host A is high and the value of CPU usage on host B is low. That
is to say, you'll see an anomaly when the CPU of host A is unusual given
the CPU of host B.

NOTE: To use the `multivariate_by_fields` property, you must also specify
`by_field_name` in your detector.

--
end::multivariate-by-fields[]

tag::n-neighbors[]
Defines the value for how many nearest neighbors each method of 
{oldetection} will use to calculate its {olscore}. When the value is not set, 
different values will be used for different ensemble members. This helps 
improve diversity in the ensemble. Therefore, only override this if you are 
confident that the value you choose is appropriate for the data set.
end::n-neighbors[]

tag::node-address[]
The network address of the node.
end::node-address[]

tag::node-datafeeds[]
For started {dfeeds} only, this information pertains to the node upon which the
{dfeed} is started.
end::node-datafeeds[]

tag::node-ephemeral-id[]
The ephemeral ID of the node.
end::node-ephemeral-id[]

tag::node-id[]
The unique identifier of the node.
end::node-id[]

tag::node-jobs[]
Contains properties for the node that runs the job. This information is
available only for open jobs.
end::node-jobs[]

tag::num-top-classes[]
Defines the number of categories for which the predicted 
probabilities are reported. It must be non-negative. If it is greater than the 
total number of categories (in the {version} version of the {stack}, it's two) 
to predict then we will report all category probabilities. Defaults to 2.
end::num-top-classes[]

tag::open-time[]
For open jobs only, the elapsed time for which the job has been open.
end::open-time[]

tag::out-of-order-timestamp-count[]
The number of input documents that are out of time sequence and outside
of the latency window. This information is applicable only when you provide data
to the {anomaly-job} by using the <<ml-post-data,post data API>>. These out of
order documents are  discarded, since jobs require time series data to be in
ascending chronological order.
end::out-of-order-timestamp-count[]

tag::outlier-fraction[]
Sets the proportion of the data set that is assumed to be outlying prior to 
{oldetection}. For example, 0.05 means it is assumed that 5% of values are real 
outliers and 95% are inliers.
end::outlier-fraction[]

tag::over-field-name[]
The field used to split the data. In particular, this property is used for 
analyzing the splits with respect to the history of all splits. It is used for 
finding unusual values in the population of all splits. For more information,
see {ml-docs}/ml-configuring-pop.html[Performing population analysis].
end::over-field-name[]

tag::partition-field-name[]
The field used to segment the analysis. When you use this property, you have 
completely independent baselines for each value of this field.
end::partition-field-name[]

tag::prediction-field-name[]
Defines the name of the prediction field in the results. 
Defaults to `<dependent_variable>_prediction`.
end::prediction-field-name[]

tag::processed-field-count[]
The total number of fields in all the documents that have been processed by the
{anomaly-job}. Only fields that are specified in the detector configuration
object contribute to this count. The timestamp is not included in this count.
end::processed-field-count[]

tag::processed-record-count[]
The number of input documents that have been processed by the {anomaly-job}.
This value includes documents with missing fields, since they are nonetheless
analyzed. If you use {dfeeds} and have aggregations in your search query, the
`processed_record_count` is the number of aggregation results processed, not the
number of {es} documents.
end::processed-record-count[]

tag::randomize-seed[]
Defines the seed to the random generator that is used to pick which documents 
will be used for training. By default it is randomly generated. Set it to a 
specific value to ensure the same documents are used for training assuming other 
related parameters (for example, `source`, `analyzed_fields`, etc.) are the 
same.
end::randomize-seed[]

tag::query[]
The {es} query domain-specific language (DSL). This value corresponds to the
query object in an {es} search POST body. All the options that are supported by
{es} can be used, as this object is passed verbatim to {es}. By default, this
property has the following value: `{"match_all": {"boost": 1}}`.
end::query[]

tag::query-delay[]
The number of seconds behind real time that data is queried. For example, if
data from 10:04 a.m. might not be searchable in {es} until 10:06 a.m., set this
property to 120 seconds. The default value is randomly selected between `60s`
and `120s`. This randomness improves the query performance when there are
multiple jobs running on the same node. For more information, see
{ml-docs}/ml-delayed-data-detection.html[Handling delayed data].
end::query-delay[]

tag::rare-category-count[]
The number of categories that match just one categorized document.
end::rare-category-count[]

tag::renormalization-window-days[]
Advanced configuration option. The period over which adjustments to the score
are applied, as new data is seen. The default value is the longer of 30 days or
100 `bucket_spans`.
end::renormalization-window-days[]

tag::results-index-name[]
A text string that affects the name of the {ml} results index. The default value
is `shared`, which generates an index named `.ml-anomalies-shared`. 
end::results-index-name[]

tag::results-retention-days[]
Advanced configuration option. The period of time (in days) that results are 
retained. Age is calculated relative to the timestamp of the latest bucket 
result. If this property has a non-null value, once per day at 00:30 (server 
time), results that are the specified number of days older than the latest 
bucket result are deleted from {es}. The default value is null, which means all
results are retained.
end::results-retention-days[]

tag::retain[]
If `true`, this snapshot will not be deleted during automatic cleanup of
snapshots older than `model_snapshot_retention_days`. However, this snapshot
will be deleted when the job is deleted. The default value is `false`.
end::retain[]

tag::script-fields[]
Specifies scripts that evaluate custom expressions and returns script fields to
the {dfeed}. The detector configuration objects in a job can contain functions
that use these script fields. For more information, see
{ml-docs}/ml-configuring-transform.html[Transforming data with script fields]
and <<request-body-search-script-fields,Script fields>>.
end::script-fields[]

tag::scroll-size[]
The `size` parameter that is used in {es} searches. The default value is `1000`.
end::scroll-size[]

tag::search-bucket-avg[]
The average search time per bucket, in milliseconds.
end::search-bucket-avg[]

tag::search-count[]
The number of searches run by the {dfeed}.
end::search-count[]

tag::search-exp-avg-hour[]
The exponential average search time per hour, in milliseconds.
end::search-exp-avg-hour[]

tag::search-time[]
The total time the {dfeed} spent searching, in milliseconds.
end::search-time[]

tag::size[]
Specifies the maximum number of {dfanalytics-jobs} to obtain. The default value 
is `100`.
end::size[]

tag::snapshot-id[]
Identifier for the model snapshot.
end::snapshot-id[]

tag::source-put-dfa[]
The configuration of how to source the analysis data. It requires an `index`.
Optionally, `query` and `_source` may be specified.
+
.Properties of `source`
[%collapsible%open]
====
`index`:::
(Required, string or array) Index or indices on which to perform the analysis.
It can be a single index or index pattern as well as an array of indices or
patterns.
+
WARNING: If your source indices contain documents with the same IDs, only the 
document that is indexed last appears in the destination index.

`query`:::
(Optional, object) The {es} query domain-specific language (<<query-dsl,DSL>>).
This value corresponds to the query object in an {es} search POST body. All the
options that are supported by {es} can be used, as this object is passed
verbatim to {es}. By default, this property has the following value:
`{"match_all": {}}`.

`_source`:::
(Optional, object) Specify `includes` and/or `excludes` patterns to select which
fields will be present in the destination. Fields that are excluded cannot be
included in the analysis.
+
.Properties of `_source`
[%collapsible%open]
=====
`includes`::::
(array) An array of strings that defines the fields that will be included in the
destination.
        
`excludes`::::
(array) An array of strings that defines the fields that will be excluded from
the destination.
=====
====
end::source-put-dfa[]

tag::sparse-bucket-count[]
The number of buckets that contained few data points compared to the expected
number of data points. If your data contains many sparse buckets, consider using
a longer `bucket_span`.
end::sparse-bucket-count[]

tag::standardization-enabled[]
If `true`, then the following operation is performed on the columns before 
computing outlier scores: (x_i - mean(x_i)) / sd(x_i). Defaults to `true`. For 
more information, see 
https://en.wikipedia.org/wiki/Feature_scaling#Standardization_(Z-score_Normalization)[this wiki page about standardization].
end::standardization-enabled[]

tag::state-anomaly-job[]
The status of the {anomaly-job}, which can be one of the following values:
+
--
* `closed`: The job finished successfully with its model state persisted. The
job must be opened before it can accept further data.
* `closing`: The job close action is in progress and has not yet completed. A
closing job cannot accept further data.
* `failed`: The job did not finish successfully due to an error. This situation
can occur due to invalid input data, a fatal error occurring during the 
analysis, or an external interaction such as the process being killed by the 
Linux out of memory (OOM) killer. If the job had irrevocably failed, it must be 
force closed and then deleted. If the {dfeed} can be corrected, the job can be 
closed and then re-opened.
* `opened`: The job is available to receive and process data.
* `opening`: The job open action is in progress and has not yet completed.
--
end::state-anomaly-job[]

tag::state-datafeed[]
The status of the {dfeed}, which can be one of the following values:
+
--
* `starting`: The {dfeed} has been requested to start but has not yet started.
* `started`: The {dfeed} is actively receiving data.
* `stopping`: The {dfeed} has been requested to stop gracefully and is
completing its final action.
* `stopped`: The {dfeed} is stopped and will not receive data until it is
re-started.
--
end::state-datafeed[]

tag::summary-count-field-name[]
If this property is specified, the data that is fed to the job is expected to be
pre-summarized. This property value is the name of the field that contains the 
count of raw data points that have been summarized. The same 
`summary_count_field_name` applies to all detectors in the job.
+
--
NOTE: The `summary_count_field_name` property cannot be used with the `metric`
function.

--
end::summary-count-field-name[]

tag::tags[]
A comma delimited string of tags. A {infer} model can have many tags, or none.
When supplied, only {infer} models that contain all the supplied tags are
returned.
end::tags[]

tag::timeout-start[]
Controls the amount of time to wait until the {dfanalytics-job} starts. Defaults 
to 20 seconds.
end::timeout-start[]

tag::timeout-stop[]
Controls the amount of time to wait until the {dfanalytics-job} stops. Defaults 
to 20 seconds.
end::timeout-stop[]

tag::time-format[]
The time format, which can be `epoch`, `epoch_ms`, or a custom pattern. The
default value is `epoch`, which refers to UNIX or Epoch time (the number of 
seconds since 1 Jan 1970). The value `epoch_ms` indicates that time is measured 
in milliseconds since the epoch. The `epoch` and `epoch_ms` time formats accept 
either integer or real values. +
+
NOTE: Custom patterns must conform to the Java `DateTimeFormatter` class.
When you use date-time formatting patterns, it is recommended that you provide
the full date, time and time zone. For example: `yyyy-MM-dd'T'HH:mm:ssX`.
If the pattern that you specify is not sufficient to produce a complete 
timestamp, job creation fails.
end::time-format[]

tag::time-span[]
The time span that each search will be querying. This setting is only applicable
when the mode is set to `manual`. For example: `3h`.
end::time-span[]

tag::timestamp-results[]
The start time of the bucket for which these results were calculated.
end::timestamp-results[]

tag::tokenizer[]
The name or definition of the <<analysis-tokenizers,tokenizer>> to use after 
character filters are applied. This property is compulsory if 
`categorization_analyzer` is specified as an object. Machine learning provides a
tokenizer called `ml_classic` that tokenizes in the same way as the
non-customizable tokenizer in older versions of the product. If you want to use 
that tokenizer but change the character or token filters, specify
`"tokenizer": "ml_classic"` in your `categorization_analyzer`.
end::tokenizer[]

tag::total-by-field-count[]
The number of `by` field values that were analyzed by the models. This value is
cumulative for all detectors in the job.
end::total-by-field-count[]

tag::total-category-count[]
The number of categories created by categorization.
end::total-category-count[]

tag::total-over-field-count[]
The number of `over` field values that were analyzed by the models. This value
is cumulative for all detectors in the job.
end::total-over-field-count[]

tag::total-partition-field-count[]
The number of `partition` field values that were analyzed by the models. This
value is cumulative for all detectors in the job.
end::total-partition-field-count[]

tag::trained-model-configs[]
An array of trained model resources, which are sorted by the `model_id` value in 
ascending order.
+
.Properties of trained model resources
[%collapsible%open]
====
`created_by`:::
(string)
Information on the creator of the trained model.

`create_time`:::
(<<time-units,time units>>)
The time when the trained model was created.

`default_field_map` :::
(object)
A string to string object that contains the default field map to use
when inferring against the model. For example, data frame analytics
may train the model on a specific multi-field `foo.keyword`.
The analytics job would then supply a default field map entry for
`"foo" : "foo.keyword"`.
+
Any field map described in the inference configuration takes precedence.

`estimated_heap_memory_usage_bytes`:::
(integer)
The estimated heap usage in bytes to keep the trained model in memory.

`estimated_operations`:::
(integer)
The estimated number of operations to use the trained model.

`license_level`:::
(string)
The license level of the trained model.

`metadata`:::
(object)
An object containing metadata about the trained model. For example, models 
created by {dfanalytics} contain `analysis_config` and `input` objects.

`model_id`:::
(string)
Idetifier for the trained model.

`tags`:::
(string)
A comma delimited string of tags. A {infer} model can have many tags, or none.

`version`:::
(string)
The {es} version number in which the trained model was created.
====
end::trained-model-configs[]

tag::training-percent[]
Defines what percentage of the eligible documents that will 
be used for training. Documents that are ignored by the analysis (for example 
those that contain arrays with more than one value) won’t be included in the 
calculation for used percentage. Defaults to `100`.
end::training-percent[]

tag::use-null[]
Defines whether a new series is used as the null series when there is no value 
for the by or partition fields. The default value is `false`.
end::use-null[]