anchor.diff:    contains the results of the analysis.
   
   `metrics`::
  -  (Optional, object) Specifies the metrics that are used for the evaluation. 
  -  Available metrics:
  +  (Optional, object) Specifies the metrics that are used for the evaluation. If 
  +  no metrics are specified, the following are returned by default: 
  +  
  +  * `auc_roc` (`include_curve`: false), 
  +  * `precision` (`at`: [0.25, 0.5, 0.75]), 
  +  * `recall` (`at`: [0.25, 0.5, 0.75]), 
  +  * `confusion_matrix` (`at`: [0.25, 0.5, 0.75]).
     
     `auc_roc`:::
       (Optional, object) The AUC ROC (area under the curve of the receiver 
  
positive.code:   (Optional, object) Specifies the metrics that are used for the evaluation. For 
    more information on `mse`, `msle`, and `huber`, consult 
    https://github.com/elastic/examples/tree/master/Machine%20Learning/Regression%20Loss%20Functions[the Jupyter notebook on regression loss functions].
    Available metrics:
  
    `mse`:::
      (Optional, object) Average squared difference between the predicted values 
  
negative.code:   This field must be defined as `nested` in the mappings.
  
  `metrics`::
    (Optional, object) Specifies the metrics that are used for the evaluation. If 
    no metrics are specificed, the following are returned by default: 
    
    * `accuracy`, 
    * `multiclass_confusion_matrix`, 
    * `precision`, 
    * `recall`.
  
    `accuracy`:::
      (Optional, object) Accuracy of predictions (per-class and overall).
  