anchor.diff:    (Optional, object) Specifies the metrics that are used for the evaluation. 
     Available metrics:
     
  -  `auc_roc`::
  +  `auc_roc`:::
       (Optional, 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`::
  +  `confusion_matrix`:::
  +    (Optional, 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]}.
  +
  +  `precision`:::
       (Optional, 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`::
  +  `recall`:::
       (Optional, 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`::
  -    (Optional, 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]}.
   
       
   [[regression-evaluation-resources]]
  
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:    "evaluation": {
        "classification": { <1>
           "actual_field": "animal_class", <2>
           "predicted_field": "ml.animal_class_prediction", <3>
           "metrics": {  
             "multiclass_confusion_matrix" : {} <4>
           }
  