[7.x][DOCS] Update example and nesting in get data frame analytics job stats API (#55612)

docs/reference/ml/anomaly-detection/apis/get-datafeed-stats.asciidoc

@@ -82,17 +82,26 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=node-datafeeds]
 --
 [%collapsible%open]
 ====
-`id`:::
+`attributes`:::
-include::{docdir}/ml/ml-shared.asciidoc[tag=node-id]
+(object)
-
+include::{docdir}/ml/ml-shared.asciidoc[tag=node-attributes]
-`name`::: The node name. For example, `0-o0tOo`.
 
 `ephemeral_id`:::
+(string)
 include::{docdir}/ml/ml-shared.asciidoc[tag=node-ephemeral-id]
 
-`transport_address`::: The host and port where transport HTTP connections are
+`id`:::
-accepted. For example, `127.0.0.1:9300`.
+(string)
-`attributes`::: For example, `{"ml.machine_memory": "17179869184"}`.
+include::{docdir}/ml/ml-shared.asciidoc[tag=node-id]
+
+`name`:::
+(string)
+The node name. For example, `0-o0tOo`.
+
+`transport_address`:::
+(string)
+include::{docdir}/ml/ml-shared.asciidoc[tag=node-transport-address]
+
 ====
 --
 

docs/reference/ml/anomaly-detection/apis/get-job-stats.asciidoc

@@ -281,8 +281,8 @@ available only for open jobs.
 [%collapsible%open]
 ====
 `attributes`:::
-(object) Lists node attributes. For example,
+(object)
-`{"ml.machine_memory": "17179869184"}`.
+include::{docdir}/ml/ml-shared.asciidoc[tag=node-attributes]
 
 `ephemeral_id`:::
 (string)
@@ -293,10 +293,12 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=node-ephemeral-id]
 include::{docdir}/ml/ml-shared.asciidoc[tag=node-id]
 
 `name`:::
-(string) The node name.
+(string)
+The node name.
 
 `transport_address`:::
-(string) The host and port where transport HTTP connections are accepted.
+(string)
+include::{docdir}/ml/ml-shared.asciidoc[tag=node-transport-address]
 ====
 //End node
 

docs/reference/ml/df-analytics/apis/get-dfanalytics-stats.asciidoc

@@ -61,12 +61,442 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=size]
 [[ml-get-dfanalytics-stats-response-body]]
 ==== {api-response-body-title}
 
-The API returns the following information:
-
 `data_frame_analytics`::
 (array)
-include::{docdir}/ml/ml-shared.asciidoc[tag=data-frame-analytics-stats]
+An array of objects that contain usage information for {dfanalytics-jobs}, which
+are sorted by the `id` value in ascending order.
++
+.Properties of {dfanalytics-job} usage resources
+[%collapsible%open]
+====
+//Begin analysis_stats
+`analysis_stats`:::
+(object)
+An object containing information about the analysis job.
++
+.Properties of `analysis_stats`
+[%collapsible%open]
+=====
+//Begin classification_stats
+`classification_stats`::::
+(object)
+An object containing information about the {classanalysis} job.
++
+.Properties of `classification_stats`
+[%collapsible%open]
+======
+//Begin class_hyperparameters
+`hyperparameters`::::
+(object)
+An object containing the parameters of the {classanalysis} job.
++
+.Properties of `hyperparameters`
+[%collapsible%open]
+=======
+`alpha`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=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`.
+
+`downsample_factor`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-downsample-factor]
+
+`eta`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-eta]
+
+`eta_growth_rate_per_tree`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-eta-growth]
+
+`feature_bag_fraction`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-feature-bag-fraction]
+
+`gamma`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-gamma]
+
+`lambda`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-lambda]
+
+`max_attempts_to_add_tree`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-attempts]
+
+`max_optimization_rounds_per_hyperparameter`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-optimization-rounds]
+
+`max_trees`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-trees]
+
+`num_folds`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-num-folds]
+
+`num_splits_per_feature`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-num-splits]
+
+`soft_tree_depth_limit`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-soft-limit]
+
+`soft_tree_depth_tolerance`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-soft-tolerance]
+=======
+//End class_hyperparameters
+
+`iteration`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-iteration]
+
+`timestamp`::::
+(date)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timestamp]
+
+//Begin class_timing_stats
+`timing_stats`::::
+(object)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats]
++
+.Properties of `timing_stats`
+[%collapsible%open]
+=======
+`elapsed_time`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats-elapsed]
+
+`iteration_time`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats-iteration]
+=======
+//End class_timing_stats
+
+//Begin class_validation_loss
+`validation_loss`::::
+(object)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss]
++
+.Properties of `validation_loss`
+[%collapsible%open]
+=======
+`fold_values`::::
+(array of strings)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss-fold]
+
+`loss_type`::::
+(string)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss-type]
+=======
+//End class_validation_loss
+======
+//End classification_stats
+
+//Begin outlier_detection_stats
+`outlier_detection_stats`::::
+(object)
+An object containing information 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
+
+`timestamp`::::
+(date)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timestamp]
+
+//Begin od_timing_stats
+`timing_stats`::::
+(object)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats]
++
+.Property of `timing_stats`
+[%collapsible%open]
+=======
+`elapsed_time`::::
+(integer)
+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 information 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]
+=======
+`alpha`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-alpha]
+
+`downsample_factor`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-downsample-factor]
+
+`eta`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-eta]
+
+`eta_growth_rate_per_tree`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-eta-growth]
+
+`feature_bag_fraction`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-feature-bag-fraction]
+
+`gamma`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-gamma]
+
+`lambda`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-lambda]
+
+`max_attempts_to_add_tree`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-attempts]
+
+`max_optimization_rounds_per_hyperparameter`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-optimization-rounds]
+
+`max_trees`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-trees]
+
+`num_folds`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-num-folds]
+
+`num_splits_per_feature`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-num-splits]
+
+`soft_tree_depth_limit`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-soft-limit]
+
+`soft_tree_depth_tolerance`::::
+(double)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-soft-tolerance]
+=======
+//End reg_hyperparameters
+
+`iteration`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-iteration]
+
+`timestamp`::::
+(date)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timestamp]
+
+//Begin reg_timing_stats
+`timing_stats`::::
+(object)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats]
++
+.Propertis of `timing_stats`
+[%collapsible%open]
+=======
+`elapsed_time`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats-elapsed]
+
+`iteration_time`::::
+(integer)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats-iteration]
+=======
+//End reg_timing_stats
+
+//Begin reg_validation_loss
+`validation_loss`::::
+(object)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss]
++
+.Properties of `validation_loss`
+[%collapsible%open]
+=======
+`fold_values`::::
+(array of strings)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss-fold]
+
+`loss_type`::::
+(string)
+include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss-type]
+=======
+//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 that provides counts for the quantity of documents skipped, used in
+training, or available for testing.
++
+.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.
++
+.Properties of `memory_usage`
+[%collapsible%open]
+=====
+`peak_usage_bytes`:::
+(long)
+The number of bytes used at the highest peak of 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.
++
+.Properties of `node`
+[%collapsible%open]
+=====
+`attributes`:::
+(object)
+include::{docdir}/ml/ml-shared.asciidoc[tag=node-attributes]
+
+`ephemeral_id`:::
+(string)
+include::{docdir}/ml/ml-shared.asciidoc[tag=node-ephemeral-id]
+
+`id`:::
+(string)
+include::{docdir}/ml/ml-shared.asciidoc[tag=node-id]
+
+`name`:::
+(string)
+The node name.
+
+`transport_address`:::
+(string)
+include::{docdir}/ml/ml-shared.asciidoc[tag=node-transport-address]
+=====
+
+`progress`:::
+(array) The progress report of the {dfanalytics-job} by phase.
++
+.Properties of phase objects
+[%collapsible%open]
+=====
+`phase`:::
+(string) Defines the phase of the {dfanalytics-job}. Possible phases: 
+`reindexing`, `loading_data`, `analyzing`, and `writing_results`.
+    
+`progress_percent`:::
+(integer) The progress that the {dfanalytics-job} has made expressed in 
+percentage.
+=====
+
+`state`:::
+(string) The status of the {dfanalytics-job}, which can be one of the following
+values: `analyzing`, `failed`, `reindexing`, `started`, `starting`, `stopped`,
+`stopping`.
+====
+//End of data_frame_analytics
 
 [[ml-get-dfanalytics-stats-response-codes]]
 ==== {api-response-codes-title}
@@ -79,11 +509,14 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=data-frame-analytics-stats]
 [[ml-get-dfanalytics-stats-example]]
 ==== {api-examples-title}
 
+The following API retrieves usage information for the
+{ml-docs}/ecommerce-outliers.html[{oldetection} {dfanalytics-job} example]:
+
 [source,console]
 --------------------------------------------------
-GET _ml/data_frame/analytics/loganalytics/_stats
+GET _ml/data_frame/analytics/ecommerce/_stats
 --------------------------------------------------
-// TEST[skip:TBD]
+// TEST[skip:Kibana sample data]
 
 
 The API returns the following results:
@@ -94,26 +527,51 @@ The API returns the following results:
   "count" : 1,
   "data_frame_analytics" : [
     {
-            "id": "loganalytics",
+      "id" : "ecommerce",
       "state" : "stopped",
       "progress" : [
         {
           "phase" : "reindexing",
-                "progress_percent": 0
+          "progress_percent" : 100
         },
         {
           "phase" : "loading_data",
-                "progress_percent": 0
+          "progress_percent" : 100
         },
         {
           "phase" : "analyzing",
-                "progress_percent": 0
+          "progress_percent" : 100
         },
         {
           "phase" : "writing_results",
-                "progress_percent": 0
+          "progress_percent" : 100
+        }
+      ],
+      "data_counts" : {
+        "training_docs_count" : 3321,
+        "test_docs_count" : 0,
+        "skipped_docs_count" : 0
+      },
+      "memory_usage" : {
+        "timestamp" : 1586905058000,
+        "peak_usage_bytes" : 279484
+      },
+      "analysis_stats" : {
+        "outlier_detection_stats" : {
+          "timestamp" : 1586905058000,
+          "parameters" : {
+            "n_neighbors" : 0,
+            "method" : "ensemble",
+            "compute_feature_influence" : true,
+            "feature_influence_threshold" : 0.1,
+            "outlier_fraction" : 0.05,
+            "standardization_enabled" : true
+          },
+          "timing_stats" : {
+            "elapsed_time" : 245
+          }
+        }
       }
-            ]
     }
   ]
 }

docs/reference/ml/ml-shared.asciidoc

@@ -371,429 +371,6 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=time-format]
 ====
 end::data-description[]
 
-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
@@ -894,6 +471,106 @@ 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[]
+Regularization factor to penalize deeper trees when training decision trees.
+end::dfas-alpha[]
+
+tag::dfas-downsample-factor[]
+The value of the downsample factor.
+end::dfas-downsample-factor[]
+
+tag::dfas-eta[]
+The value of the eta hyperparameter.
+end::dfas-eta[]
+
+tag::dfas-eta-growth[]
+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[]
+The fraction of features that is used when selecting a random bag for each 
+candidate split.
+end::dfas-feature-bag-fraction[]
+
+tag::dfas-gamma[]
+Regularization factor to penalize trees with large numbers of nodes.
+end::dfas-gamma[]
+
+tag::dfas-iteration[]
+The number of iterations on the analysis.
+end::dfas-iteration[]
+
+tag::dfas-lambda[]
+Regularization factor to penalize large leaf weights.
+end::dfas-lambda[]
+
+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[]
+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[]
+The maximum number of trees in the forest.
+end::dfas-max-trees[]
+
+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[]
+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[]
+Tree depth tolerance is used for calculating the tree depth penalty. This is a 
+soft limit, it can be exceeded.
+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[]
@@ -1334,6 +1011,10 @@ 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.
@@ -1352,6 +1033,10 @@ 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[]