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-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::allow-no-match-models[]
Specifies what to do when the request:
+
--
* Contains wildcard expressions and there are no models 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 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-models[]

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::assignment-memory-basis[]
Where should the memory requirement used for deciding which node the job
will run on come from? The possible values are:
+
--
* `model_memory_limit`: The job's memory requirement will be calculated on
the basis that its model memory will grow to the `model_memory_limit`
specified in the `analysis_limits` of its config.
* `current_model_bytes`: The job's memory requirement will be calculated on
the basis that its current model memory size is a good reflection of what
it will be in the future.
* `peak_model_bytes`: The job's memory requirement will be calculated on
the basis that its peak model memory size is a good reflection of what
the model size will be in the future.
--
end::assignment-memory-basis[]

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-ad-finding-anomalies.html#ml-ad-bucket-span[Bucket span].
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::{es-repo-dir}/ml/ml-shared.asciidoc[tag=char-filter]

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

`filter`::::
(array of strings or objects)
include::{es-repo-dir}/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::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.
end::chunking-config[]

tag::class-assignment-objective[]
Defines the objective to optimize when assigning class labels:
`maximize_accuracy` or `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::compute-feature-influence[]
Specifies whether the feature influence calculation is enabled. Defaults to
`true`.
end::compute-feature-influence[]

tag::custom-preprocessor[]
(Optional, Boolean)
Boolean value indicating if the analytics job created the preprocessor
or if a user provided it. This adjusts the feature importance calculation.
When `true`, the feature importance calculation returns importance for the
processed feature. When `false`, the total importance of the original field
is returned. Default is `false`.
end::custom-preprocessor[]

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. {kib} refers to custom rules as _job rules_. 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::daily-model-snapshot-retention-after-days[]
Advanced configuration option, which affects the automatic removal of old model
snapshots for this job. It specifies a period of time (in days) after which only
the first snapshot per day is retained. This period is relative to the timestamp
of the most recent snapshot for this job. Valid values range from `0` to
`model_snapshot_retention_days`. For new jobs, the default value is `1`. For
jobs created before version 7.8.0, the default value matches
`model_snapshot_retention_days`. For more information, refer to
{ml-docs}/ml-ad-finding-anomalies.html#ml-ad-model-snapshots[Model snapshots].
end::daily-model-snapshot-retention-after-days[]

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::{es-repo-dir}/ml/ml-shared.asciidoc[tag=time-format]
====
end::data-description[]

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::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}.
end::delayed-data-check-config[]

tag::delayed-data-check-config-check-window[]
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`.
end::delayed-data-check-config-check-window[]

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

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::dfas-alpha[]
Advanced configuration option. {ml-cap} uses loss guided tree growing, which
means that the decision trees grow where the regularized loss decreases most
quickly. This parameter affects loss calculations by acting as a multiplier of
the tree depth. Higher alpha values result in shallower trees and faster
training times. By default, this value is calculated during hyperparameter
optimization. It must be greater than or equal to zero.
end::dfas-alpha[]

tag::dfas-downsample-factor[]
Advanced configuration option. Controls the fraction of data that is used to
compute the derivatives of the loss function for tree training. A small value
results in the use of a small fraction of the data. If this value is set to be
less than 1, accuracy typically improves. However, too small a value may result
in poor convergence for the ensemble and so require more trees. For more
information about shrinkage, refer to
{wikipedia}/Gradient_boosting#Stochastic_gradient_boosting[this wiki article].
By default, this value is calculated during hyperparameter optimization. It
must be greater than zero and less than or equal to 1.
end::dfas-downsample-factor[]

tag::dfas-early-stopping-enabled[]
Advanced configuration option.
Specifies whether the training process should finish if it is not finding any
better performing models. If disabled, the training process can take significantly
longer and the chance of finding a better performing model is unremarkable.
By default, early stoppping is enabled.
end::dfas-early-stopping-enabled[]

tag::dfas-eta-growth[]
Advanced configuration option. Specifies the rate at which `eta` increases for
each new tree that is added to the forest. For example, a rate of 1.05
increases `eta` by 5% for each extra tree. By default, this value is calculated
during hyperparameter optimization. It must be between 0.5 and 2.
end::dfas-eta-growth[]

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

tag::dfas-feature-processors[]
Advanced configuration option. A collection of feature preprocessors that modify
one or more included fields. The analysis uses the resulting one or more
features instead of the original document field. However, these features are
ephemeral; they are not stored in the destination index. Multiple
`feature_processors` entries can refer to the same document fields. Automatic
categorical {ml-docs}/ml-feature-encoding.html[feature encoding] still occurs
for the fields that are unprocessed by a custom processor or that have
categorical values. Use this property only if you want to override the automatic
feature encoding of the specified fields. Refer to
{ml-docs}/ml-feature-processors.html[{dfanalytics} feature processors] to learn
more.
end::dfas-feature-processors[]

tag::dfas-feature-processors-feat-name[]
The resulting feature name.
end::dfas-feature-processors-feat-name[]

tag::dfas-feature-processors-field[]
The name of the field to encode.
end::dfas-feature-processors-field[]

tag::dfas-feature-processors-frequency[]
The configuration information necessary to perform frequency encoding.
end::dfas-feature-processors-frequency[]

tag::dfas-feature-processors-frequency-map[]
The resulting frequency map for the field value. If the field value is missing
from the `frequency_map`, the resulting value is `0`.
end::dfas-feature-processors-frequency-map[]

tag::dfas-feature-processors-multi[]
The configuration information necessary to perform multi encoding. It allows
multiple processors to be changed together. This way the output of a processor
can then be passed to another as an input.
end::dfas-feature-processors-multi[]

tag::dfas-feature-processors-multi-proc[]
The ordered array of custom processors to execute. Must be more than 1.
end::dfas-feature-processors-multi-proc[]

tag::dfas-feature-processors-ngram[]
The configuration information necessary to perform n-gram encoding. Features
created by this encoder have the following name format:
`<feature_prefix>.<ngram><string position>`. For example, if the
`feature_prefix` is `f`, the feature name for the second unigram in a string is
`f.11`.
end::dfas-feature-processors-ngram[]

tag::dfas-feature-processors-ngram-feat-pref[]
The feature name prefix. Defaults to `ngram_<start>_<length>`.
end::dfas-feature-processors-ngram-feat-pref[]

tag::dfas-feature-processors-ngram-field[]
The name of the text field to encode.
end::dfas-feature-processors-ngram-field[]

tag::dfas-feature-processors-ngram-length[]
Specifies the length of the n-gram substring. Defaults to `50`. Must be greater
than `0`.
end::dfas-feature-processors-ngram-length[]

tag::dfas-feature-processors-ngram-ngrams[]
Specifies which n-grams to gather. It’s an array of integer values where the
minimum value is 1, and a maximum value is 5.
end::dfas-feature-processors-ngram-ngrams[]

tag::dfas-feature-processors-ngram-start[]
Specifies the zero-indexed start of the n-gram substring. Negative values are
allowed for encoding n-grams of string suffixes. Defaults to `0`.
end::dfas-feature-processors-ngram-start[]

tag::dfas-feature-processors-one-hot[]
The configuration information necessary to perform one hot encoding.
end::dfas-feature-processors-one-hot[]

tag::dfas-feature-processors-one-hot-map[]
The one hot map mapping the field value with the column name.
end::dfas-feature-processors-one-hot-map[]

tag::dfas-feature-processors-target-mean[]
The configuration information necessary to perform target mean encoding.
end::dfas-feature-processors-target-mean[]

tag::dfas-feature-processors-target-mean-default[]
The default value if field value is not found in the `target_map`.
end::dfas-feature-processors-target-mean-default[]

tag::dfas-feature-processors-target-mean-map[]
The field value to target mean transition map.
end::dfas-feature-processors-target-mean-map[]

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

tag::dfas-max-attempts[]
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[]
Advanced configuration option.
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.
By default, this value is calculated during hyperparameter optimization.
end::dfas-max-optimization-rounds[]

tag::dfas-num-folds[]
The maximum number of folds for the cross-validation procedure.
end::dfas-num-folds[]

tag::dfas-num-splits[]
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[]
Advanced configuration option. {ml-cap} uses loss guided tree growing, which
means that the decision trees grow where the regularized loss decreases most
quickly. This soft limit combines with the `soft_tree_depth_tolerance` to
penalize trees that exceed the specified depth; the regularized loss increases
quickly beyond this depth. By default, this value is calculated during
hyperparameter optimization. It must be greater than or equal to 0.
end::dfas-soft-limit[]

tag::dfas-soft-tolerance[]
Advanced configuration option. This option controls how quickly the regularized
loss increases when the tree depth exceeds `soft_tree_depth_limit`. By default,
this value is calculated during hyperparameter optimization. It must be greater
than or equal to 0.01.
end::dfas-soft-tolerance[]

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

tag::dfas-timing-stats[]
An object containing time statistics about the {dfanalytics-job}.
end::dfas-timing-stats[]

tag::dfas-timing-stats-elapsed[]
Runtime of the analysis in milliseconds.
end::dfas-timing-stats-elapsed[]

tag::dfas-timing-stats-iteration[]
Runtime of the latest iteration of the analysis in milliseconds.
end::dfas-timing-stats-iteration[]

tag::dfas-validation-loss[]
An object containing information about validation loss.
end::dfas-validation-loss[]

tag::dfas-validation-loss-fold[]
Validation loss values for every added decision tree during the forest growing
procedure.
end::dfas-validation-loss-fold[]

tag::dfas-validation-loss-type[]
The type of the loss metric. For example, `binomial_logistic`.
end::dfas-validation-loss-type[]

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 a better generalization error.
However, larger forests cause slower training. For more information about
shrinkage, refer to
{wikipedia}/Gradient_boosting#Shrinkage[this wiki article].
By default, this value is calculated during hyperparameter optimization. It must
be a value between 0.001 and 1.
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. Defaults to `false`, which means interim results are included.
end::exclude-interim-results[]

tag::failed-category-count[]
The number of times that categorization wanted to create a new category but
couldn't because the job had hit its `model_memory_limit`. This count does not
track which specific categories failed to be created. Therefore you cannot use
this value to determine the number of unique categories that were missed.
end::failed-category-count[]

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. By default, this
value is calculated during hyperparameter optimization.
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::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::exclude-generated[]
Indicates if certain fields should be removed from the configuration on
retrieval. This allows the configuration to be in an acceptable format to be retrieved
and then added to another cluster. Default is false.
end::exclude-generated[]

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::from-models[]
Skips the specified number of models. The default value is `0`.
end::from-models[]

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 data set. Multiplies a linear penalty associated with the size
of individual trees in the forest. A high gamma value causes training to prefer
small trees. A small gamma value results in larger individual trees and slower
training. By default, this value is calculated during hyperparameter
optimization. It must be a nonnegative value.
end::gamma[]

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

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 the {ml} nodes need to have the
`remote_cluster_client` role.

--
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::runtime-mappings[]
Specifies runtime fields for the datafeed search.
+
--
For example:
```
{
  "day_of_week": {
    "type": "keyword",
    "script": {
      "source": "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ROOT))"
    }
  }
}
```

--
end::runtime-mappings[]

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}/ml-feature-importance.html[{feat-imp}] values per document. Defaults
to 0 which means no {feat-imp} 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-classification-prediction-field-type[]
Specifies the type of the predicted field to write.
Valid values are: `string`, `number`, `boolean`. When `boolean` is provided
`1.0` is transformed to `true` and `0.0` to `false`.
end::inference-config-classification-prediction-field-type[]

tag::inference-config-nlp-tokenization[]
Indicates the tokenization to perform and the desired settings.
end::inference-config-nlp-tokenization[]

tag::inference-config-nlp-tokenization-bert[]
BERT-style tokenization is to be performed with the enclosed settings.
end::inference-config-nlp-tokenization-bert[]

tag::inference-config-nlp-tokenization-bert-do-lower-case[]
Specifies if the tokenization lower case the text sequence when building the
tokens.
end::inference-config-nlp-tokenization-bert-do-lower-case[]

tag::inference-config-nlp-tokenization-bert-truncate[]
Indicates how tokens are truncated when they exceed `max_sequence_length`.
The default value is `first`.
+
--
* `none`: No truncation occurs; the inference request receives an error.
* `first`: Only the first sequence is truncated.
* `second`: Only the second sequence is truncated. If there is just one sequence,
					 that sequence is truncated.
--

NOTE: For `zero_shot_classification`, the hypothesis sequence is always the second
sequence. Therefore, do not use `second` in this case.

end::inference-config-nlp-tokenization-bert-truncate[]

tag::inference-config-nlp-tokenization-bert-with-special-tokens[]
Tokenize with special tokens. The tokens typically included in BERT-style tokenization are:
+
--
* `[CLS]`: The first token of the sequence being classified.
* `[SEP]`: Indicates sequence separation.
--
end::inference-config-nlp-tokenization-bert-with-special-tokens[]

tag::inference-config-nlp-tokenization-bert-max-sequence-length[]
Specifies the maximum number of tokens allowed to be output by the tokenizer.
The default for BERT-style tokenization is `512`.
end::inference-config-nlp-tokenization-bert-max-sequence-length[]

tag::inference-config-nlp-vocabulary[]
The configuration for retreiving the vocabulary of the model. The vocabulary is
then used at inference time. This information is usually provided automatically
by storing vocabulary in a known, internally managed index.
end::inference-config-nlp-vocabulary[]

tag::inference-config-nlp-fill-mask[]
Configuration for a fill_mask natural language processing (NLP) task. The
fill_mask task works with models optimized for a fill mask action. For example,
for BERT models, the following text may be provided: "The capital of France is
[MASK].". The response indicates the value most likely to replace `[MASK]`. In
this instance, the most probable token is `paris`.
end::inference-config-nlp-fill-mask[]

tag::inference-config-ner[]
Configures a named entity recognition (NER) task. NER is a special case of token
classification. Each token in the sequence is classified according to the
provided classification labels. Currently, the NER task requires the
`classification_labels` Inside-Outside-Beginning (IOB) formatted labels. Only
person, organization, location, and miscellaneous are supported.
end::inference-config-ner[]

tag::inference-config-pass-through[]
Configures a `pass_through` task. This task is useful for debugging as no
post-processing is done to the inference output and the raw pooling layer
results are returned to the caller.
end::inference-config-pass-through[]

tag::inference-config-text-classification[]
A text classification task. Text classification classifies a provided text
sequence into previously known target classes. A specific example of this is
sentiment analysis, which returns the likely target classes indicating text
sentiment, such as "sad", "happy", or "angry".
end::inference-config-text-classification[]

tag::inference-config-text-embedding[]
Text embedding takes an input sequence and transforms it into a vector of
numbers. These embeddings capture not simply tokens, but semantic meanings and
context. These embeddings can be used in a <<dense-vector,dense vector>> field
for powerful insights.
end::inference-config-text-embedding[]

tag::inference-config-regression-num-top-feature-importance-values[]
Specifies the maximum number of
{ml-docs}/ml-feature-importance.html[{feat-imp}] values per document.
By default, it is zero and no {feat-imp} 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::inference-config-results-field-processor[]
The field that is added to incoming documents to contain the inference
prediction. Defaults to the `results_field` value of the {dfanalytics-job} that was
used to train the model, which defaults to `<dependent_variable>_prediction`.
end::inference-config-results-field-processor[]

tag::inference-config-zero-shot-classification[]
Configures a zero-shot classification task. Zero-shot classification allows for
text classification to occur without pre-determined labels. At inference time,
it is possible to adjust the labels to classify. This makes this type of model
and task exceptionally flexible.
+
--
If consistently classifying the same labels, it may be better to use a
fine-tuned text classification model.
--
end::inference-config-zero-shot-classification[]

tag::inference-config-zero-shot-classification-classification-labels[]
The classification labels used during the zero-shot classification. Classification
labels must not be empty or null and only set at model creation. They must be all three
of ["entailment", "neutral", "contradiction"].

NOTE: This is NOT the same as `labels` which are the values that zero-shot is attempting to
      classify.
end::inference-config-zero-shot-classification-classification-labels[]

tag::inference-config-zero-shot-classification-hypothesis-template[]
This is the template used when tokenizing the sequences for classification.
+
--
The labels replace the `{}` value in the text. The default value is:
`This example is {}.`
--
end::inference-config-zero-shot-classification-hypothesis-template[]

tag::inference-config-zero-shot-classification-labels[]
The labels to classify. Can be set at creation for default labels, and
then updated during inference.
end::inference-config-zero-shot-classification-labels[]

tag::inference-config-zero-shot-classification-multi-label[]
Indicates if more than one `true` label is possible given the input.
This is useful when labeling text that could pertain to more than one of the
input labels. Defaults to `false`.
end::inference-config-zero-shot-classification-multi-label[]

tag::inference-metadata-feature-importance-feature-name[]
The feature for which this importance was calculated.
end::inference-metadata-feature-importance-feature-name[]
tag::inference-metadata-feature-importance-magnitude[]
The average magnitude of this feature across all the training data.
This value is the average of the absolute values of the importance
for this feature.
end::inference-metadata-feature-importance-magnitude[]
tag::inference-metadata-feature-importance-max[]
The maximum importance value across all the training data for this
feature.
end::inference-metadata-feature-importance-max[]
tag::inference-metadata-feature-importance-min[]
The minimum importance value across all the training data for this
feature.
end::inference-metadata-feature-importance-min[]

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 data set. Multiplies an L2 regularization term which applies to
leaf weights of the individual trees in the forest. A high lambda value causes
training to favor small leaf weights. This behavior makes the prediction
function smoother at the expense of potentially not being able to capture
relevant relationships between the features and the {depvar}. A small lambda
value results in large individual trees and slower training. By default, this
value is calculated during hyperparameter optimization. It must be a nonnegative
value.
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 decision trees in
the forest. The maximum value is 2000. By default, this value is calculated
during hyperparameter optimization.
end::max-trees[]

tag::max-trees-trained-models[]
The maximum number of decision trees in the forest. The maximum value is 2000.
By default, this value is calculated during hyperparameter optimization.
end::max-trees-trained-models[]

tag::method[]
The method that {oldetection} uses. Available methods are `lof`, `ldof`,
`distance_kth_nn`, `distance_knn`, and `ensemble`. The default value is
`ensemble`, which means that {oldetection} uses an ensemble of different methods
and normalises and combines their individual {olscores} to obtain the overall
{olscore}.
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 when the {dfeed} does not use aggregations.
* `manual`: Chunking is applied according to the specified `time_span`. Use this
mode when the {dfeed} uses aggregations.
* `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 model.
end::model-id[]

tag::model-id-or-alias[]
The unique identifier of the trained model or a model alias.
end::model-id-or-alias[]

tag::model-memory-limit-ad[]
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`. If the
`xpack.ml.max_model_memory_limit` setting has a value greater than `0` and less
than `1024mb`, however, that value is used instead. If
`xpack.ml.max_model_memory_limit` is not set, but
`xpack.ml.use_auto_machine_memory_percent` is set, then the default
`model_memory_limit` will be set to the largest size that could be assigned in
the cluster, capped at `1024mb`. The default value is relatively small to
ensure that high resource usage is a conscious decision. If you have jobs that
are expected to analyze high cardinality fields, you will likely need to use a
higher value.
+
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 you specify a value for the `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 value. For more information, see <<ml-settings>>.
end::model-memory-limit-ad[]

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
you specify a value for the `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 value. 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. Additionally, in
categorization jobs no further category examples will be stored.
* `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-annotations-enabled[]
If true, enables calculation and storage of the model change annotations
for each entity that is being analyzed. Defaults to `enabled`.
end::model-plot-config-annotations-enabled[]

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-prune-window[]
Advanced configuration option.
Affects the pruning of models that have not been updated for the given time
duration. The value must be set to a multiple of the `bucket_span`. If set too
low, important information may be removed from the model. Typically, set to
`30d` or longer. If not set, model pruning only occurs if the model memory
status reaches the soft limit or the hard limit.
end::model-prune-window[]

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, which affects the automatic removal of old model
snapshots for this job. It specifies the maximum period of time (in days) that
snapshots are retained. This period is relative to the timestamp of the most
recent snapshot for this job. The default value is `10`, which means snapshots
ten days older than the newest snapshot are deleted. For more information, refer
to {ml-docs}/ml-ad-finding-anomalies.html#ml-ad-model-snapshots[Model snapshots].
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}
uses to calculate its {olscore}. When the value is not set, different values are
used for different ensemble members. This default behavior helps improve the
diversity in the ensemble; only override it 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-attributes[]
Lists node attributes such as `ml.machine_memory` or `ml.max_open_jobs` settings.
end::node-attributes[]

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::node-transport-address[]
The host and port where transport HTTP connections are accepted.
end::node-transport-address[]

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 have a timestamp chronologically
preceding the start of the current anomaly detection bucket offset by
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[]
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-populations.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::peak-model-bytes[]
The peak number of bytes of memory ever used by the models.
end::peak-model-bytes[]

tag::per-partition-categorization[]
Settings related to how categorization interacts with partition fields.
end::per-partition-categorization[]

tag::per-partition-categorization-enabled[]
To enable this setting, you must also set the partition_field_name property to
the same value in every detector that uses the keyword mlcategory. Otherwise,
job creation fails.
end::per-partition-categorization-enabled[]

tag::per-partition-categorization-stop-on-warn[]
This setting can be set to true only if per-partition categorization is enabled.
If true, both categorization and subsequent anomaly detection stops for
partitions where the categorization status changes to `warn`. This setting makes
it viable to have a job where it is expected that categorization works well for
some partitions but not others; you do not pay the cost of bad categorization
forever in the partitions where it works badly.
end::per-partition-categorization-stop-on-warn[]

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 for the random generator that is used to pick training data. By
default, it is randomly generated. Set it to a specific value to use the same
training data each time you start a job (assuming other related parameters such
as `source` and `analyzed_fields` 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. Annotations generated by the system also count as results
for retention purposes; they are deleted after the same number of days as
results. Annotations added by users are retained forever.
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 <<script-fields,Script fields>>.
end::script-fields[]

tag::scroll-size[]
The `size` parameter that is used in {es} searches when the {dfeed} does not use
aggregations. The default value is `1000`. The maximum value is the value of
`index.max_result_window` which is 10,000 by default.
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::size-models[]
Specifies the maximum number of models to obtain. The default value
is `100`.
end::size-models[]

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

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`, 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 about this concept, see
{wikipedia}/Feature_scaling#Standardization_(Z-score_Normalization)[Wikipedia].
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 trained model can have many tags, or none.
When supplied, only trained 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_standard` that tokenizes in a way that has been
determined to produce good categorization results on a variety of log
file formats for logs in English. If you want to use that tokenizer but
change the character or token filters, specify `"tokenizer": "ml_standard"`
in your `categorization_analyzer`. Additionally, the `ml_classic` tokenizer
is available, which tokenizes in the same way as the non-customizable
tokenizer in old versions of the product (before 6.2). `ml_classic` was
the default categorization tokenizer in versions 6.2 to 7.13, so if you
need categorization identical to the default for jobs created in these
versions, 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::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[]

tag::verbose[]
Defines whether the stats response should be verbose. The default value is `false`.
end::verbose[]