anchor.diff:  belongs.
   
   `actual_field`::
  -  (Required, string) The field of the `index` which contains the ground truth. 
  -  The data type of this field must be keyword.
  -  
  -`metrics`::
  -  (Required, object) Specifies the metrics that are used for the evaluation. 
  -  Available metric is `multiclass_confusion_matrix`.
  +  (Required, string) The field of the `index` which contains the `ground truth`.
  +  The data type of this field must be categorical.
     
   `predicted_field`::
     (Required, string) The field in the `index` that contains the predicted value, 
  -  in other words the results of the {classanalysis}. The data type of this field 
  -  is string. You need to add `.keyword` to the predicted field name (the name 
  -  you put in the {classanalysis} object as `prediction_field_name` or the 
  -  default value of the same field if you didn't specified explicitly). For 
  -  example, `predicted_field` : `ml.animal_class_prediction.keyword`.
  +  in other words the results of the {classanalysis}.
  +
  +`metrics`::
  +  (Optional, object) Specifies the metrics that are used for the evaluation.
  +  Available metrics:
  +
  +  `accuracy`:::
  +    (Optional, object) Accuracy of predictions (per-class and overall).
  +
  +  `multiclass_confusion_matrix`:::
  +    (Optional, object) Multiclass confusion matrix.
  +
  +  `precision`:::
  +    (Optional, object) Precision of predictions (per-class and average).
  +
  +  `recall`:::
  +    (Optional, object) Recall of predictions (per-class and average).
   
   
   ////
  
positive.code: performed.
  
  `query`::
  (Optional, object) A query clause that retrieves a subset of data from the 
  source index. See <<query-dsl>>.
  
  [[ml-evaluate-dfanalytics-resources]]
  ==== {dfanalytics-cap} evaluation resources
  
  [[binary-sc-resources]]
  ===== Binary soft classification configuration objects
  
  Binary soft classification evaluates the results of an analysis which outputs 
  the probability that each document belongs to a certain class. For example, in 
  
negative.code: ==== {api-request-body-title}
  
  `evaluation`::
  (Required, object) Defines the type of evaluation you want to perform.
  See <<ml-evaluate-dfanalytics-resources>>.
  +
  --
  Available evaluation types:
  
  * `binary_soft_classification`
  * `regression`
  * `classification`
  
  --
  
  `index`::
  