anchor.diff:    in other words the results of the {regression} analysis.
     
   `metrics`::
  -  (Required, object) Specifies the metrics that are used for the evaluation. 
  -  Available metrics are `r_squared` and `mean_squared_error`.
  -  
  +  (Optional, object) Specifies the metrics that are used for the evaluation.
  +  Available metrics:
  +
  +  `mean_squared_error`:::
  +    (Optional, object) Average squared difference between the predicted values and the actual (`ground truth`) value.
  +    For more information, read https://en.wikipedia.org/wiki/Mean_squared_error[this wiki article].
  +
  +  `r_squared`:::
  +    (Optional, object) Proportion of the variance in the dependent variable that is predictable from the independent variables.
  +    For more information, read https://en.wikipedia.org/wiki/Coefficient_of_determination[this wiki article].
  +
  +
     
   [[classification-evaluation-resources]]
   ==== {classification-cap} evaluation objects
  
positive.code: ==== {api-request-body-title}
  
  `evaluation`::
  (Required, 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. See <<ml-evaluate-dfanalytics-resources>>.
  +
  --
  Available evaluation types:
  * `binary_soft_classification`
  * `regression`
  * `classification`
  --
  
  `index`::
  
negative.code:    "evaluation": {
        "classification": { <1>
           "actual_field": "animal_class", <2>
           "predicted_field": "ml.animal_class_prediction", <3>
           "metrics": {  
             "multiclass_confusion_matrix" : {} <4>
           }
  