[DOCS] Adds data frame analytics API and evaluate API resource documentation (#43972)

This PR adds the resource documentation of the data frame analytics APIs and the evaluate API to the ML API doc pool.

docs/reference/ml/apis/dfanalyticsresources.asciidoc

@@ -0,0 +1,108 @@
+[role="xpack"]
+[testenv="platinum"]
+[[ml-dfanalytics-resources]]
+=== {dfanalytics-cap} job resources
+
+{dfanalytics-cap} resources relate to APIs such as <<put-dfanalytics>> and
+<<get-dfanalytics>>.	
+
+[discrete]	
+[[ml-dfanalytics-properties]]	
+==== {api-definitions-title}
+
+`analysis`::
+  (object) The type of analysis that is performed on the `source`. For example: 
+  `outlier_detection`. For more information, see <<dfanalytics-types>>.
+  
+`analyzed_fields`::
+  (object) You can specify both `includes` and/or `excludes` patterns. If 
+  `analyzed_fields` is not set, only the relevant fields will be included. For 
+  example all the numeric fields for {oldetection}.
+
+`dest`::
+  (object) The destination configuration of the analysis. For more information, 
+  see <<dfanalytics-dest-resources>>.
+
+`id`::
+  (string) The unique 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. This property 
+  is informational; you cannot change the identifier for existing jobs.
+  
+`model_memory_limit`::
+  (string) 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>>.
+
+`source`::
+  (object) The source configuration, consisting of `index` and optionally a 
+  `query`. For more information, see <<dfanalytics-source-resources>>.
+
+[[dfanalytics-types]]
+==== Analysis objects
+
+{dfanalytics-cap} resources contain `analysis` objects. For example, when you
+create a {dfanalytics-job}, you must define the type of analysis it performs.
+  
+[discrete]
+[[oldetection-resources]]
+===== {oldetection-cap} configuration objects 
+
+An {oldetection} configuration object has the following properties:
+
+[discrete]
+[[oldetection-properties]]
+==== {api-definitions-title}
+
+`n_neighbors`::
+  (integer) Defines the value for how many nearest neighbors each method of 
+  {oldetection} will use to calculate its {olscore}. When the value is 
+  not set, the system will dynamically detect an appropriate value.
+
+`method`::
+  (string) 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}. 
+  Available methods are `lof`, `ldof`, `distance_kth_nn`, `distance_knn`.
+
+`feature_influence_threshold`:: 
+  (double) The minimum {olscore} that a document needs to have in order to 
+  calculate its {fiscore}. 
+  Value range: 0-1 (`0.1` by default).
+  
+[[dfanalytics-dest-resources]]
+==== Dest configuration objects
+
+{dfanalytics-cap} resources contain `dest` objects. For example, when you
+create a {dfanalytics-job}, you must define its destination.
+
+[discrete]
+[[dfanalytics-dest-properties]]
+==== {api-definitions-title}
+
+`index`::
+  (string) The name of the index in which to store the results of the 
+  {dfanalytics-job}.
+
+`results_field`::
+  (string) The name of the field in which to store the results of the analysis. 
+  The default value is `ml`.
+
+[[dfanalytics-source-resources]]
+==== Source configuration objects
+
+The `source` configuration object has the following properties:
+
+`index`::
+  (array) An array of index names 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 {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}}`.

docs/reference/ml/apis/evaluate-dfanalytics.asciidoc

@@ -8,15 +8,9 @@
 <titleabbrev>Evaluate {dfanalytics}</titleabbrev>
 ++++
 
-experimental[]
+Evaluates the {dfanalytics} for an annotated index.
 
-Evaluates the executed analysis on an index that is already annotated with a 
+experimental[]
-field that contains the results of the analytics (the `ground truth`) for each 
-{dataframe} row. Evaluation is typically done via calculating a set of metrics 
-that capture various aspects of the quality of the results over the data for 
-which we have the `ground truth`. For different types of analyses different 
-metrics are suitable. This API packages together commonly used metrics for 
-various analyses.
 
 [[ml-evaluate-dfanalytics-request]]
 ==== {api-request-title}
@@ -30,6 +24,19 @@ various analyses.
 information, see {stack-ov}/security-privileges.html[Security privileges] and 
 {stack-ov}/built-in-roles.html[Built-in roles].
 
+[[ml-evaluate-dfanalytics-desc]]
+==== {api-description-title}
+
+This API evaluates the executed analysis on an index that is already annotated
+with a field that contains the results of the analytics (the `ground truth`)
+for each {dataframe} row.
+
+Evaluation is typically done by calculating a set of metrics that capture various aspects of the quality of the results over the data for which you have the
+`ground truth`.
+
+For different types of analyses different metrics are suitable. This API
+packages together commonly used metrics for various analyses.
+
 [[ml-evaluate-dfanalytics-request-body]]
 ==== {api-request-body-title}
 
@@ -38,8 +45,22 @@ information, see {stack-ov}/security-privileges.html[Security privileges] and
   
 `evaluation` (Required)::
   (object) Defines the type of evaluation you want to perform. For example: 
-  `binary_soft_classification`.
+  `binary_soft_classification`. See <<ml-evaluate-dfanalytics-resources>>.
-  See Evaluate API resources.
+  
+[[ml-evaluate-dfanalytics-results]]
+==== {api-response-body-title}
+
+`binary_soft_classification`::
+  (object) If you chose to do binary soft classification, the API returns the
+  following evaluation metrics:
+  
+`auc_roc`::: TBD
+
+`confusion_matrix`::: TBD
+  
+`precision`::: TBD
+
+`recall`::: TBD
 
 [[ml-evaluate-dfanalytics-example]]
 ==== {api-examples-title}

docs/reference/ml/apis/evaluateresources.asciidoc

@@ -0,0 +1,63 @@
+[role="xpack"]
+[testenv="platinum"]
+[[ml-evaluate-dfanalytics-resources]]
+=== {dfanalytics-cap} evaluation resources
+
+Evaluation configuration objects relate to the <<evaluate-dfanalytics>>.
+
+[discrete]
+[[ml-evaluate-dfanalytics-properties]]
+==== {api-definitions-title}
+
+`evaluation`::
+  (object) Defines the type of evaluation you want to perform. The value of this 
+  object can be different depending on the type of evaluation you want to 
+  perform. For example, it can contain <<binary-sc-resources>>.
+
+[[binary-sc-resources]]
+==== Binary soft classification configuration objects
+
+Binary soft classification evaluates the results of an analysis which outputs 
+the probability that each {dataframe} row belongs to a certain class. For 
+example, in the context of outlier detection, the analysis outputs the 
+probability whether each row is an outlier.
+
+[discrete]
+[[binary-sc-resources-properties]]
+===== {api-definitions-title}
+
+`actual_field`::
+  (string) The field of the `index` which contains the `ground 
+  truth`. The data type of this field can be boolean or integer. If the data 
+  type is integer, the value has to be either `0` (false) or `1` (true).
+
+`predicted_probability_field`::
+  (string) The field of the `index` that defines the probability of whether the 
+  item belongs to the class in question or not. It's the field that contains the 
+  results of the analysis.
+
+`metrics`::
+  (object) Specifies the metrics that are used for the evaluation. Available 
+  metrics:
+  
+  `auc_roc`::
+    (object) The AUC ROC (area under the curve of the receiver operating 
+    characteristic) score and optionally the curve.
+    Default value is {"includes_curve": false}.
+    
+  `precision`::
+    (object) Set the different thresholds of the {olscore} at where the metric 
+    is calculated.
+    Default value is {"at": [0.25, 0.50, 0.75]}.
+  
+  `recall`::
+    (object) Set the different thresholds of the {olscore} at where the metric 
+    is calculated.
+    Default value is {"at": [0.25, 0.50, 0.75]}.
+  
+  `confusion_matrix`::
+    (object) Set the different thresholds of the {olscore} at where the metrics 
+    (`tp` - true positive, `fp` - false positive, `tn` - true negative, `fn` - 
+    false negative) are calculated.
+    Default value is {"at": [0.25, 0.50, 0.75]}.
+  

docs/reference/ml/apis/get-dfanalytics.asciidoc

@@ -46,6 +46,10 @@ You can get information for all {dfanalytics-jobs} by using _all, by specifying
   these options, the API returns information for the first hundred
   {dfanalytics-jobs}.
   
+`allow_no_match` (Optional)::
+  (boolean) If `false` and the `data_frame_analytics_id` does not match any 
+  {dfanalytics-job} an error will be returned. The default value is `true`.
+
 [[ml-get-dfanalytics-query-params]]
 ==== {api-query-parms-title}
 
@@ -61,6 +65,13 @@ You can get information for all {dfanalytics-jobs} by using _all, by specifying
   (integer) Specifies the maximum number of {dfanalytics-jobs} to obtain. The 
   default value is `100`.
   
+[[ml-get-dfanalytics-results]]
+==== {api-response-body-title}
+
+`data_frame_analytics`::
+  (array) An array of {dfanalytics-job} resources. For more information, see
+  <<ml-dfanalytics-resources>>.
+
 [[ml-get-dfanalytics-example]]
 ==== {api-examples-title}
 

docs/reference/ml/apis/put-dfanalytics.asciidoc

@@ -59,21 +59,20 @@ and mappings.
 
 `analysis` (Required)::
   (object) Defines the type of {dfanalytics} you want to perform on your source 
-  index. For example: `outlier_detection`. 
+  index. For example: `outlier_detection`. See <<dfanalytics-types>>.
-  See {oldetection} resources.
   
 `analyzed_fields` (Optional)::
   (object) You can specify both `includes` and/or `excludes` patterns. If 
-  `analyzed_fields` is not set, only the relevant fileds will be included. For 
+  `analyzed_fields` is not set, only the relevant fields will be included. For 
-  example all the numeric fields for {oldetection}.
+  example, all the numeric fields for {oldetection}.
   
 `dest` (Required)::
   (object) The destination configuration, consisting of `index` and optionally 
-  `results_field` (`ml` by default).
+  `results_field` (`ml` by default). See <<dfanalytics-dest-resources>>.
   
 `source` (Required)::
   (object) The source configuration, consisting of `index` and optionally a 
-  `query`.
+  `query`. See <<dfanalytics-source-resources>>. 
 
 [[ml-put-dfanalytics-example]]
 ==== {api-examples-title}

docs/reference/rest-api/defs.asciidoc

@@ -8,7 +8,9 @@ These resource definitions are used in APIs related to {ml-features} and
 * <<ml-calendar-resource,Calendars>>
 * <<ml-datafeed-resource,{dfeeds-cap}>>
 * <<ml-datafeed-counts,{dfeed-cap} counts>>
+* <<ml-dfanalytics-resources,{dfanalytics-cap}>>
 * <<data-frame-transform-resource,{dataframe-transforms-cap}>>
+* <<ml-evaluate-dfanalytics-resources,Evaluate {dfanalytics}>>
 * <<ml-filter-resource,Filters>>
 * <<ml-job-resource,Jobs>>
 * <<ml-jobstats,Job statistics>>
@@ -19,7 +21,9 @@ These resource definitions are used in APIs related to {ml-features} and
 
 include::{es-repo-dir}/ml/apis/calendarresource.asciidoc[]
 include::{es-repo-dir}/ml/apis/datafeedresource.asciidoc[]
+include::{es-repo-dir}/ml/apis/dfanalyticsresources.asciidoc[]
 include::{es-repo-dir}/data-frames/apis/transformresource.asciidoc[]
+include::{es-repo-dir}/ml/apis/evaluateresources.asciidoc[]
 include::{es-repo-dir}/ml/apis/filterresource.asciidoc[]
 include::{es-repo-dir}/ml/apis/jobresource.asciidoc[]
 include::{es-repo-dir}/ml/apis/jobcounts.asciidoc[]